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Integrating Artificial Intelligence and Blockchain Technology for Enhanced US Homeland Security
... Aspects such as the Office of Personnel Management breach, which targeted approximately 21.5 million government employees in 2015, and the SolarWinds supply chain attack experienced by several federal organizations in 2020 showed how the challenges regarding cyberspace threats are continuing to grow. From 2017 to 2020, the reported losses from cybercrime contributing to the Federal Bureau of Investigation's Internet Crime Complaint Centre (IC3) rose from $ 1.4 billion to over $4.2 billion demonstrating the rising economic losses from cybercrimes (Rele et al., 2023). It was in this period that the comprehensive National Institute of Standards and Technology (NIST) Cybersecurity Framework was released in 2014 constituting an organized framework for approaching the management of cybersecurity risk from the important infrastructural sectors in the USA. ...
... Based on the implementation analyses of such systems, organizations that adopted integrated systems recorded approximately 73.2% fewer cases of unauthorized access as compared to traditional privacy controls through the use of cryptographic access control which is supported by AI-based system that analyzes possible access anomalies. The largest improvement was realized in this domain as organizations in New York recorded 79.5% decrease in privacy violations after adopting the aspects, meeting a basic tenet of trust and compliance in financial organizations that deal with sensitive financial data (Rele et al., 2023). This is due to the integrate of the blockchain's cryptographic feature where access control handle in contrast of the AI's contextual analysis to establish flexible privacy boundaries rather than generic permission that often fails to address the legitimate access complexity. ...
... In this sector, different blockchain platform choices are given for their performance, especially when resource limited, with lighter ones including IOTA (27.3%) and Energy Web Chain (51.3%). Rele et al. (2023) also established that it is common among these organizations to use middleware integrating approaches (62.7 %) to ensure that the boundary of operational technologies systems and the security monitoring components are coated, thereby meeting the fundamental need of averting the disruption of crucial operations by the security implementations. Among the implementations with a high probability of incorporating edge processing capabilities is the energy sector (72.3%) the rationale being the location of the energy infrastructure and network connectivity issues experienced in some areas. ...
Introduction: This review examines the integration of artificial intelligence (AI) and blockchain technologies for secure data management in cybersecurity across the United States. Due to the growing instances of cyber threats today, organizations and government agencies are looking at new ways of technological integration to enhance the aspect of data protection. This paper aims to establish how AI and blockchain functions together to provide secure solutions to the current cybersecurity threats. Materials and Methods: This article adopts the secondary research approach that involves the use of surveys from peer-reviewed articles, industry reports, and analytical studies on AI-blockchain frameworks. The assumptions made are based on expert opinions from the interviews and case studies that have been compiled from reputed institutions such as the Cybersecurity and Infrastructure Security Agency (CISA), the Massachusetts Institute of Technology (MIT), and other Fortune 500 firms. From previous research, quantitative data is conducted and pertains to performance, the implementation rate, and sectorial outcomes in the financial service, health and the critical infrastructure sectors. Results: Based on the findings, it can be concluded that integrated AI-blockchain systems are 37% more effective in mitigating APT attacks as opposed to antecedent security models. The organizations that have adopted such technologies have showed improved statistics of reduced data breach incidents by 42% and unauthorized access attempts by 56%. As far as the adoption rate, there are several leaders – and they have proved to be the financial institutions in New York and California with health care providers in Massachusetts and Texas not far behind. The technical pros are better out of the ordinary detection (up to 89% accuracy rates) and cryptographic audited trails, which considerably improve the response time. Discussion: The review identifies four distinct implementation models emerging across different sectors and regions in the United States. Financial institutions primarily leverage these integrated technologies for transaction verification and fraud detection, while healthcare organizations focus on secure patient data management and access control. Government agencies, particularly in Virginia and Maryland, employ these systems for critical infrastructure protection and threat intelligence sharing. Key implementation factors include regulatory compliance requirements, organizational security maturity, and industry-specific threat landscapes. Conclusion: The integration of AI and blockchain technologies provide a new lea for viewer cybersecurity strategies in the United States. But all these need some considerations in form of rules and regulations, technical difficulties, and challenges of qualified human resource. The overall recommendations for increasing the level of adoption consist of sectoral best practices to be followed in implementing the discussed concepts and frameworks, cross-organizational collaboration models, and educational programs to foster the acquisition of adequate workforce competencies in these converging technologies.
The rise in artificial intelligence (AI) and machine learning (ML) in cryptocurrency trading has precipitated complex ethical considerations, demanding a thorough exploration of responsible regulatory approaches. This research expands upon this need by employing a consequentialist theoretical framework, emphasizing the outcomes of AI and ML’s deployment within the sector and its effects on stakeholders. Drawing on critical case studies, such as SBF and FTX, and conducting an extensive review of relevant literature, this study explores the ethical implications of AI and ML in the context of cryptocurrency trading. It investigates the necessity for novel regulatory methods that address the unique characteristics of digital assets alongside existing legalities, such as those about fraud and insider trading. The author proposes a typology framework for AI and ML trading by comparing consequentialism to other ethical theories applicable to AI and ML use in cryptocurrency trading. By applying a consequentialist lens, this study underscores the significance of balancing AI and ML’s transformative potential with ethical considerations to ensure market integrity, investor protection, and overall well-being in cryptocurrency trading.
Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate repetitive tasks, accelerate threat detection and response, and improve the accuracy of their actions to strengthen the security posture against various security issues and cyberattacks. This article presents a systematic literature review and a detailed analysis of AI use cases for cybersecurity provisioning. The review resulted in 2395 studies, of which 236 were identified as primary. This article classifies the identified AI use cases based on a NIST cybersecurity framework using a thematic analysis approach. This classification framework will provide readers with a comprehensive overview of the potential of AI to improve cybersecurity in different contexts. The review also identifies future research opportunities in emerging cybersecurity application areas, advanced AI methods, data representation, and the development of new infrastructures for the successful adoption of AI-based cybersecurity in today's era of digital transformation and polycrisis.
Smart grid integrates computer, communication, and sensing technologies into existing power grid networks to achieve significant informatization-related advantages. It will provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. Smart grids (SG) replace conventional grids by integrating various operational measures, including smart automation appliances, smart meters, and renewable energy sources. Regarding energy management and resolving energy issues, SG is one of the most cutting-edge and potentially game-changing innovations. Even still, its complexity suggests that decentralization may provide significant gains. Because of its increasing digitization and interconnectedness, it is also vulnerable to cyber threats. Blockchain, in this sense, is a potential SG paradigm solution that provides several great benefits. Even though blockchains have been widely discussed to decentralize and strengthen smart grid cybersecurity, they have not yet been researched in depth from an application and architectural standpoint. Blockchain-enabled SG applications are the subject of an in-depth research investigation. Electric vehicles (EVs), home automation, energy management systems, etc., are only a few of the many examples that have prompted the proposal of blockchain designs for their respective use cases. Information communication network security is of paramount importance. However, this evolving system raises cybersecurity issues. This paper aims to guide researchers in the right manner so they may build blockchain-based, secure, distributed SG applications in the future. This article also summarizes cybersecurity threats pertaining to smart grids. It starts with a description of a blockchain followed by the blockchain infrastructure, challenges, and solutions for different smart grid applications. A look back at the tried-and-true methods of securing a power grid is offered, and then it discusses the newer and more complex cybersecurity threats to the smart grid. In addition, models of common cyberattacks are presented, and the methods of defense against them are examined.
In this article, we explore the security politics of EU database interoperability, inquiring how knowledge infrastructures underpin European security integration. Sitting at the intersection of Science and Technology Studies (STS) and critical approaches to European security, we unpack the co-constitutive relation between database anxieties and interoperability mechanisms. By database anxieties, we refer to what European institutions identify as the main epistemic and operational concerns that emerge from the current use of databases by security authorities across Europe. These anxieties are expected to be resolved by mechanisms that foster interoperability. We argue that the relation between database anxieties and interoperability mechanisms shapes the novel conditions of possibility for European security integration in a datafied world. While far-reaching in technological terms, interoperability is not about introducing a new overarching system, but about the management, re-organisation and re-purposing of datasets. Such formatting matters politically because it eventually informs sovereign acts of policing and mobility control.
Artificial intelligence (AI) and blockchain are the two disruptive technologies emerging from the Fourth Industrial Revolution (IR4.0) that have introduced radical shifts in the industry. The amalgamation of AI and blockchain holds tremendous potential to create new business models enabled through digitalization. Although research on the application and convergence of AI and blockchain exists, our understanding of the utility of its integration for business remains fragmented. To address this gap, this study aims to characterize the applications and benefits of integrated AI and blockchain platforms across different verticals of business. Using bibliometric analysis, this study reveals the most influential articles on the subject based on their publications, citations, and importance in the intellectual network. Using content analysis, this study sheds light on the subject’s intellectual structure, which is underpinned by four major thematic clusters focusing on supply chains, healthcare, secure transactions, and finance and accounting. The study concludes with 10 application areas in business that can benefit from these technologies.
Background: Blockchain is a digitally managed, distributed, and decentralized ledger used to record transactions in an immutable format. Its characteristics in providing trust, transparency, and traceability make it attractive for applications where transactions are involved. Originally intended to support financial transactions, the technology has gained attention even in non-financial sectors such as health care, manufacturing, retail, and government services. Methods: For centuries, the various functions of manufacturing industries have worked based on the relationship and trust that they have with their upstream and downstream stakeholders. In addition, as the conventional factories are growing into giant gigafactories, the participation of several intermediaries further complicates the supply chain and logistics operations. Hence, the article aims to provide a comprehensive overview of the role of blockchain technology in addressing supply chain and logistics-related challenges by analyzing, organizing, and reviewing the literature. Results: The study shows that blockchain technology can transform the supply chain and logistics into secure, agile, trusted, and transparent functions. A conceptualized application scenario demonstrates the benefits of blockchain technology in providing provenance and traceability to critical products. Conclusions: In particular, a private or permissioned blockchain is suitable for multi-organizational businesses such as supply chain and logistics. In addition, IoT-blockchain integration, smart contracts, and asset tracking has immense benefits in the future.
Artificial intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus, AI-based modeling is the key to build automated, intelligent, and smart systems according to today’s needs. To solve real-world issues, various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on “AI-based Modeling” with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.
Chronic diseases are a growing concern worldwide, with nearly 25% of adults suffering from one or more chronic health conditions, thus placing a heavy burden on individuals, families, and healthcare systems. With the advent of the “Smart Healthcare” era, a series of cutting-edge technologies has brought new experiences to the management of chronic diseases. Among them, smart wearable technology not only helps people pursue a healthier lifestyle but also provides a continuous flow of healthcare data for disease diagnosis and treatment by actively recording physiological parameters and tracking the metabolic state. However, how to organize and analyze the data to achieve the ultimate goal of improving chronic disease management, in terms of quality of life, patient outcomes, and privacy protection, is an urgent issue that needs to be addressed. Artificial intelligence (AI) can provide intelligent suggestions by analyzing a patient’s physiological data from wearable devices for the diagnosis and treatment of diseases. In addition, blockchain can improve healthcare services by authorizing decentralized data sharing, protecting the privacy of users, providing data empowerment, and ensuring the reliability of data management. Integrating AI, blockchain, and wearable technology could optimize the existing chronic disease management models, with a shift from a hospital-centered model to a patient-centered one. In this paper, we conceptually demonstrate a patient-centric technical framework based on AI, blockchain, and wearable technology and further explore the application of these integrated technologies in chronic disease management. Finally, the shortcomings of this new paradigm and future research directions are also discussed.
Document management systems in AEC projects manage important project documents such as schedules, RFIs, and change orders. Hence, security concerns in document management systems especially involving data integrity of documents and records may have a severe effect on a project in terms of money and the reputations of project participants. Therefore, in this research blockchain technology is leveraged to facilitate data integrity in document management for construction applications through - (1) irreversible and irrevocable approval workflow logic via smart contract technology, (2) irreversible recording of document changes via blockchain ledger technology, and (3) document version history integrity via a blockchain-based data structure. A prototype of the proposed smart contract framework was developed using Hyperledger fabric and evaluated. The scalability of the proposed framework to support document version integrity was also evaluated and discussed. A formulation based on existing literature is developed to evaluate the cost viability of the proposed framework.
Technology advances neither enhances nor detracts from cognitive human bias since its existence over thousands of years. However, skewness in conventional artificial intelligence mechanisms leads to algorithmic mishaps. Understanding the interconnection between human and machine, that algorithms are subject to bias through interactions, and the output of algorithmic bias forms a source of inspiration to the U.S. Intelligence Community (IC). As a result of emerging technologies such as artificial intelligence (AI) and its subset of machine learning, complex cybercrimes raises security concerns and highlights pertinent considerations for national security; infectious diseases can be a national security concern and facilitate bioterrorism and the production of biological weapons; and automation systems in the form of unmanned aerial systems (UAS) can have national security implications as an attractive tool for criminals and nefarious actors. This article uses grounded theory to analyse congressional hearing reports and related official documents. Increasing our understanding of the process leads to output attainment since AI operates at the speed of light and more data than we can humanly manage accessible. The present research intends to contribute to international relations, international studies, security and strategic studies, and science and technology studies.
With opportunities brought by the Internet of Things (IoT), it is quite a challenge to maintain concurrency and privacy when a huge number of resource-constrained distributed devices are involved. Blockchain have become popular for its benefits, including decentralization, persistence, immutability, auditability, and consensus. Great attention has been received by the IoT based on the construction of distributed file systems worldwide. A new generation of IoT-based distributed file systems has been proposed with the integration of Blockchain technology, such as the Swarm and Interplanetary File System. By using IoT, new technical challenges, such as Credibility, Harmonization, large-volume data, heterogeneity, and constrained resources are arising. To ensure data security in IoT, centralized access control technologies do not provide credibility. In this work, we propose an attribute-based access control model for the IoT. The access control lists are not required for each device by the system. It enhances access management in terms of effectiveness. Moreover, we use blockchain technology for recording the attribute, avoiding data tempering, and eliminating a single point of failure at edge computing devices. IoT devices control the user’s environment as well as his or her private data collection; therefore, the exposure of the user’s personal data to non-trusted private and public servers may result in privacy leakage. To automate the system, smart contracts are used for data accessing, whereas Proof of Authority is used for enhancing the system’s performance and optimizing gas consumption. Through smart contracts, ciphertext can be stored on a blockchain by the data owner. Data can only be decrypted in a valid access period, whereas in blockchains, the trace function is achieved by the storage of invocation and the creation of smart contracts. Scalability issues can also be resolved by using the multichain blockchain. Eventually, it is concluded from the simulation results that the proposed system is efficient for IoT.
When dealing with cloud services, there are important security requirements that are highly recommended to be achieved, notably, access control. Cloud services have provided several mechanisms based on traditional solutions for access management. All have ultimately failed as forgery-proof methods. Reality is a word replete with unauthorized access breaches. Therefore, the need for new grounds for robust access management in cloud services is still urgently needed. In this paper, we stand on the blockchain technology to introduce the BC-ABAC as a reliable, flexible, transparent, and fine-grained access control that preserves user identity privacy and ensures accountability. To show the feasibility of the solution, we developed a prototype on a permissioned blockchain and we conducted some experiments to measure the scalability of the solution. Finally, we give a threat model analysis.
This paper discussed how artificial intelligence (AI) may be used to address cybersecurity issues. Although secure communication between people, services, and devices is possible via centralized digital organizations, there are significant dangers associated with the digital revolution. Data mining, profiling, and exploitation are can be done without the users' expressed permission [1]. The continued provision of central solutions from service providers is wasteful in duplication, has significant security deficiencies, and is difficult for users. Digital identity authentication and verification are critical for ensuring the privacy and security of dispersed digital identities. However, the present body of knowledge is deficient in terms of thorough studies on unified communications aspects as well as user privacy and data security measures included in identity management systems. Digital identity management and verification are almost certainly the most promising of the many emerging applications of blockchain technology. In 2018, individual security breaches affected billions of individuals worldwide. Unquestionably, more secure methods of storing, exchanging, and validating sensitive data are required. In this respect, the development of blockchain solutions for identification systems may offer helpful solutions to some of the issues that most centralized databases face. Physical devices and human impedance are inadequate to manage and protect against cybercrime. Criminals utilize the Internet to perpetrate a wide variety of cybercrimes [1]. The experts seek assistance in preventing assaults and security breaches, as well as responding to attacks. The primary objective of cybersecurity is to minimize assaults via the use of AI technologies. There are existing applications of artificial intelligence in cybersecurity, and certain cybersecurity problems may be addressed via the use of AI methods, as well as some beneficial AI applications [2]. INTRODUCTION Artificial Intelligence (AI) is increasingly being incorporated into business processes and systems. But not all industries are equally sophisticated: the IT and telecommunications industry are the most sophisticated sector for AI adoption while cars fall short of their rates. According to a recent worldwide study, which surveyed over 4500 policy-makers across various industries, 45% of big enterprises, and 29% of small and medium-sized enterprises reported AI use [2]. AI will be becoming more essential to handle cyber-threats in the area of cybersecurity: fact, the market is projected to expand [2]. At the same time, the use of AI is not without dangers: over 60 percent of AI businesses recognize that AI creates the most significant cybersecurity concerns [2, 3]. AI, being a general-purpose, dual-purpose technology, has the potential to be both a boon and a bane for cybersecurity. The fact that AI is employed as a sword (e.g. to promote malevolent aggression) as well as a shield confirms this (to counter cybersecurity risks) [3]. With an added twist: because the use of AI for national security purposes experiences many limitations, particularly as government agencies (and the European Union) keep moving to monitor and control high-risk applications and encourage greater AI use, on the attack side, the most malicious applications keep increasing, the cost of new applications falls, and the 'threat landscape' becomes denser with each passing day. This paper will address some of how artificial intelligence applies in cybersecurity.
The smart city emerged as a model with the rapid growth of robust information and communication technology and the development of ubiquitous sensing technology. A smart city offers enhanced social facilities, transport and accessibility while promoting sustainability by using different sensors to gather data from the surroundings. The data collected can then be used to control urban infrastructure, such as traffic congestion, water supply, environmental monitoring, food services, and more. The smart city can track people’s actions and deliver intelligent travel, intelligent healthcare, entertainment, and other services. Dynamic data change includes intelligent and systems solutions for the functioning of these networks to ensure confusion about events in smart cities. Recent advances in machine learning and artificial information allow intelligent cities to effectively deliver services through a reduction in resource consumption. Cloud-based machine learning models enable resource-restricted devices to interconnect and optimize efficiency. The emerging data collection and device designs are targeted at reducing energy savings rather than risks to privacy and security. Thus, the security and privacy concerns remain as intelligent city networks not only collect information from heterogeneous nodes which are the weakest link and susceptible to cyber-attack. In this chapter, we address security issues in smart city applications; and corresponding countermeasures using artificial intelligence and machine learning. Some attempts to address these protection and privacy problems are then presented for smart health, transport, and smart energy.
With the deployment of the fifth-generation (5G) wireless networks worldwide, research on the sixth-generation (6G) wireless communications has commenced. It is expected that 6G networks can accommodate a vast of heterogeneous devices and infrastructures with enhanced efficiency and security over diverse, e.g., spectrum, computing and storage, resources. However, this goal is impeded by a number of trust related issues that are often neglected in network designs. Blockchain, as an innovated and revolutionary technology uprised in the recent decade, provides a promising solution. Building on its nature of decentralization, transparency, anonymity, immutability, traceability and resiliency, blockchain can establish cooperative trust among separate network entities and facilitate, e.g., efficient resource sharing, trusted data interaction, secure access control, privacy protection, and tracing, certification and supervision functionalities for wireless networks, thus presenting a new paradigm towards 6G. This paper is dedicated to blockchain enabled wireless communication technologies. We first provide a brief introduction to the fundamentals of blockchain, and then we conduct a comprehensive investigation of the most recent efforts in incorporating blockchain into wireless communications from several aspects. Importantly, we further propose a unified framework of blockchain radio access network (B-RAN) as a trustworthy and secure paradigm for 6G networking by utilizing blockchain technologies with enhanced efficiency and security. The critical elements of B-RAN, such as consensus mechanisms, smart contract, trustworthy access, mathematical modeling, cross-network sharing, data tracking and auditing, and intelligent networking, are elaborated. We also provide the prototype design of B-RAN along with the latest experimental results.
The agricultural sector is still lagging behind from all other sectors in terms of using the newest technologies. For production, the latest machines are being introduced and adopted. However, pre-harvest and post-harvest processing are still done by following traditional methodologies while tracing, storing, and publishing agricultural data. As a result, farmers are not getting deserved payment, consumers are not getting enough information before buying their product, and intermediate person/processors are increasing retail prices. Using blockchain, smart contracts, and IoT devices, we can fully automate the process while establishing absolute trust among all these parties. In this research, we explored the different aspects of using blockchain and smart contracts with the integration of IoT devices in pre-harvesting and post-harvesting segments of agriculture. We proposed a system that uses blockchain as the backbone while IoT devices collect data from the field level, and smart contracts regulate the interaction among all these contributing parties. The system implementation has been shown in diagrams and with proper explanations. Gas costs of every operation have also been attached for a better understanding of the costs. We also analyzed the system in terms of challenges and advantages. The overall impact of this research was to show the immutable, available, transparent, and robustly secure characteristics of blockchain in the field of agriculture while also emphasizing the vigorous mechanism that the collaboration of blockchain, smart contract, and IoT presents.
Blockchain technology is the most trusted all-in-one cryptosystem that provides a framework for securing transactions over networks due to its irreversibility and immutability characteristics. Blockchain network, as a decentralized infrastructure, has drawn the attention of various startups, administrators, and developers. This system preserves transactions from tampering and provides a tracking tool for tracing past network operations. A personal health record (PHR) system permits patients to control and share data concerning their health conditions by particular peoples. In the case of an emergency, the patient is unable to approve the emergency staff access to the PHR. Furthermore, a history record management system of the patient's PHR is required, which exhibits hugely private personal data (e.g., modification date, name of user, last health condition, etc.). In this paper, we suggest a healthcare management framework that employs blockchain technology to provide a tamper protection application by considering safe policies. These policies involve identifying extensible access control, auditing, and tamper resistance in an emergency scenario. Our experiments demonstrated that the proposed framework affords superior performance compared to the state-of-the-art healthcare systems concerning accessibility, privacy, emergency access control, and data auditing.
Today, Blockchain technology is emerging to support high secured performance computing. To improve privacy and security factors of allocate, storage and manage resources in edge computing, blockchain technology can be used to guarantee safe data transmission and communications in Internet of Things (IoT). Due to heterogeneity of resources to cover a range number of IoT applications, resource allocation has a key role for increasing performance of the IoT systems in edge computing. Also, a blockchain-based resource allocation model can support Quality of Service (QoS) for IoT applications to ensure a high secured communication environment in edge computing. This paper reviews different existing green energy strategies based on blockchain technology in mobile crowdsensing. Mobile crowdsensing refers to all computational aspects related to smart city, smart home-care, smart grid, smart transportations, smart medical systems, smart industry, manufacturing, and smart farming in Internet of Things (IoT) environments. This review aims to classify principles and technical aspects of green energy strategies based on blockchain technology in mobile crowdsensing with respect to technical aspects, applied algorithms, evaluation factors and evaluation environments. Based on Systematic Literature Review (SLR), we selected 37 high potential and key relevant research studies to consider in this review. Based on analytical results, 35% of them were considered as private-based blockchain methods, 33% of them were applied to the public-based blockchain methods, 24% of them were considered as consortium-based blockchain methods, and 8% of them were as hybrid-based blockchain methods.
Security presents itself as one of the biggest threats to the enabling and the deployment of the Internet of Things (IoT). Security challenges are evident in light of recent cybersecurity attacks that targeted major internet service providers and crippled a significant portion of the entire Internet by taking advantage of faulty and ill-protected embedded devices. Many of these devices reside at home networks with user-administrators who are not familiar with network security best practices, making them easy targets for the attackers. Therefore, security solutions are needed to navigate the insecure and untrusted public networks by automating protections through affordable and accessible first-hand network information sharing. This paper proposes and implements a proof of concept (PoC) to secure Internet Service Providers (ISPs), home networks, and home-based IoT devices using blockchain technologies. The results obtained support the idea of a distributed cyber threat intelligence data sharing network capable of protecting various stakeholders.
Advances in information technology are digitizing the healthcare domain with the aim of improved medical services, diagnostics, continuous monitoring using wearables, etc., at reduced costs. This digitization improves the ease of computation, storage and access of medical records which enables better treatment experiences for patients. However, it comes with a risk of cyber attacks and security and privacy concerns on this digital data. In this work, we propose a Blockchain based solution for healthcare records to address the security and privacy concerns which are currently not present in existing e-Health systems. This work also explores the potential of building trusted Artificial Intelligence models over Blockchain in e-Health, where a transparent platform for consent-based data sharing is designed. Provenance of the consent of individuals and traceability of data sources used for building and training the AI model is captured in an immutable distributed data store. The audit trail of the data access captured using Blockchain provides the data owner to understand the exposure of the data. It also helps the user to understand the revenue models that could be built on top of this framework for commercial data sharing to build trusted AI models.
Over the past several years, many healthcare applications have been developed to enhance the healthcare industry. Recent advancements in information technology and blockchain technology have revolutionized electronic healthcare research and industry. The innovation of miniaturized healthcare sensors for monitoring patient vital signs has improved and secured the human healthcare system. The increase in portable health devices has enhanced the quality of health-monitoring status both at an activity/fitness level for self-health tracking and at a medical level, providing more data to clinicians with potential for earlier diagnosis and guidance of treatment. When sharing personal medical information, data security and comfort are essential requirements for interaction with and collection of electronic medical records. However, it is hard for current systems to meet these requirements because they have inconsistent security policies and access control structures. The new solutions should be directed towards improving data access, and should be managed by the government in terms of privacy and security requirements to ensure the reliability of data for medical purposes. Blockchain paves the way for a revolution in the traditional pharmaceutical industry and benefits from unique features such as privacy and transparency of data. In this paper, we propose a novel platform for monitoring patient vital signs using smart contracts based on blockchain. The proposed system is designed and developed using hyperledger fabric, which is an enterprise-distributed ledger framework for developing blockchain-based applications. This approach provides several benefits to the patients, such as an extensive, immutable history log, and global access to medical information from anywhere at any time. The Libelium e-Health toolkit is used to acquire physiological data. The performance of the designed and developed system is evaluated in terms of transaction per second, transaction latency, and resource utilization using a standard benchmark tool known as Hyperledger Caliper. It is found that the proposed system outperforms the traditional health care system for monitoring patient data.
Electronic healthcare (eHealth) identity management (IdM) is a pivotal feature in the eHealth system. Distributed ledger technology (DLT) is an emerging technology that can achieve agreements of transactional data states in a decentralized way. Building identity management systems using Blockchain can enable patients to fully control their own identity and provide increased confidence in data immutability and availability. This paper presents the state of the art of decentralized identity management using Blockchain and highlights the possible opportunities for adopting the decentralized identity management approaches for future health identity systems. First, we summarize eHealth identity management scenarios. Furthermore, we investigate the existing decentralized identity management solutions and present decentralized identity models. In addition, we discuss the current decentralized identity projects and identify new challenges based on the existing solutions and the limitations when applying it to healthcare as a particular use case.
Cloud computing plays a critical role in modern society and enables a range of applications from infrastructure to social media. Such system must cope with varying load and evolving usage reflecting societies’ interaction and dependency on automated computing systems whilst satisfying Quality of Service (QoS) guarantees. Enabling these systems are a cohort of conceptual technologies, synthesized to meet demand of evolving computing applications. In order to understand current and future challenges of such system, there is a need to identify key technologies enabling future applications. In this study, we aim to explore how three emerging paradigms (Blockchain, IoT and Artificial Intelligence) will influence future cloud computing systems. Further, we identify several technologies driving these paradigms and invite international experts to discuss the current status and future directions of cloud computing. Finally, we proposed a conceptual model for cloud futurology to explore the influence of emerging paradigms and technologies on evolution of cloud computing.
Recently, Artificial Intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. Also with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities for machines similar to humans. In this paper, we present a detailed survey on blockchain applications for AI. We review the literature, tabulate, and summarize the emerging blockchain applications, platforms, and protocols specifically targeting AI area. We also identify and discuss open research challenges of utilizing blockchain technologies for AI.
In recent years, development of blockchain technology and virtual currencies (VCs) have been followed not just by the media and industry professionals all over the world – there rarely could be found a person who has not ever heard the term ‘bitcoin’. Despite the popularity of VCs, there is a lack of comprehensible information in the Latvian language about what the virtual currency (VC) is, as well the concepts of digital and VCs are often misused as synonyms. The research is based on the analysis of special literature and scientific publications on the system of VCs. The aim of the research: to explore the concept of VCs in the modern economy. General scientific research methods are used in the research: the method of monographic or descriptive research, the comparative analysis method for studying the concept of VCs, the classification, legal regulation, and future development possibilities. The results of the research show that VCs are a type of digital currency, though, the opposite statement is not correct. Thus, all VCs are digital, but not all digital currencies are virtual. Exploration of the legal framework of VCs suggests that it is at an early stage of development. With the increasing number of VCs and along with strengthening of the legal framework of VCs, the issue of possible directions of the future development of VCs is raised. Two points of view dominate: the future currency or payment system, for example, smart contracts.
Internet of Things (IoT) devices are rapidly becoming ubiquitous while IoT services are becoming pervasive. Their success has not gone unnoticed and the number of threats and attacks against IoT devices and services are on the increase as well. Cyber-attacks are not new to IoT, but as IoT will be deeply interwoven in our lives and societies, it is becoming necessary to step up and take cyber defense seriously. Hence, there is a real need to secure IoT, which has consequently resulted in a need to comprehensively understand the threats and attacks on IoT infrastructure. This paper is an attempt to classify threat types, besides analyze and characterize intruders and attacks facing IoT devices and services.
Purpose
The purpose of this paper is to understand how information technology (IT) is used to enhance supply chain performance.
Design/methodology/approach
A large‐scale survey and semi‐structured interviews were used to collect industry data.
Findings
Two distinct dimensions to information sharing – connectivity and willingness – are identified and analyzed. Both dimensions are found to impact operational performance and to be critical to the development of a real information sharing capability. However, many companies are found to have placed most of their emphasis on connectivity, often overlooking the willingness construct. As a result, information sharing seldom delivers on its promise to enable the creation of the cohesive supply chain team.
Research limitations
Despite the extensive data collection, the research represents a snapshot of practice. Replication from a longitudinal perspective would help define how IT is evolving to enable supply chain management.
Practical implications
A roadmap is presented to help guide IT development and investment decisions.
Originality/value
The research presents a two‐by‐two matrix to help managers and academics understand the related nature of connectivity and willingness. A roadmap is presented to help guide IT development and investment decisions.
This research investigated information systems security policy (ISSP) compliance by drawing upon two relevant theories i.e. the theory of planned behavior (TPB) and the protection motivation theory (PMT). A research model that fused constituents of the aforementioned theories was proposed and validated. Relevant hypotheses were developed to test the research conceptualization. Data analysis was performed using the partial least squares (PLS) technique. Using a survey of 124 business managers and IS professionals, this study showed that factors such as self-efficacy, attitude toward compliance, subjective norms, response efficacy and perceived vulnerability positively influence ISSP behavioral compliance intentions of employees. The data analysis did not support perceived severity and response cost as being predictors of ISSP behavioral compliance intentions. The study’s implications for research and practice are discussed.
In the last few years, the trend in health care of embracing artificial intelligence (AI) has dramatically changed the medical landscape. Medical centres have adopted AI applications to increase the accuracy of disease diagnosis and mitigate health risks. AI applications have changed rules and policies related to healthcare practice and work ethics. However, building trustworthy and explainable AI (XAI) in healthcare systems is still in its early stages. Specifically, the European Union has stated that AI must be human-centred and trustworthy, whereas in the healthcare sector, low methodological quality and high bias risk have become major concerns. This study endeavours to offer a systematic review of the trustworthiness and explainability of AI applications in healthcare, incorporating the assessment of quality, bias risk, and data fusion to supplement previous studies and provide more accurate and definitive findings. Likewise, 64 recent contributions on the trustworthiness of AI in healthcare from multiple databases (i.e., ScienceDirect, Scopus, Web of Science, and IEEE Xplore) were identified using a rigorous literature search method and selection criteria. The considered papers were categorised into a coherent and systematic classification including seven categories: explainable robotics, prediction, decision support, blockchain, transparency, digital health, and review. In this paper, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth the challenges, motivations, and recommendations. In this study a systematic science mapping analysis in order to reorganise and summarise the results of earlier studies to address the issues of trustworthiness and objectivity was also performed. Moreover, this work has provided decisive evidence for the trustworthiness of AI in health care by presenting eight current state-of-the-art critical analyses regarding those more relevant research gaps. In addition, to the best of our knowledge, this study is the first to investigate the feasibility of utilising trustworthy and XAI applications in healthcare, by incorporating data fusion techniques and connecting various important pieces of information from available healthcare datasets and AI algorithms. The analysis of the revised contributions revealed crucial implications for academics and practitioners, and then potential methodological aspects to enhance the trustworthiness of AI applications in the medical sector were reviewed. Successively, the theoretical concept and current use of 17 XAI methods in health care were addressed. Finally, several objectives and guidelines were provided to policymakers to establish electronic health-care systems focused on achieving relevant features such as legitimacy, morality, and robustness. Several types of information fusion in healthcare were focused on in this study, including data, feature, image, decision, multimodal, hybrid, and temporal.
Natural disasters are often unpredictable and therefore there is a need for quick and effective response to save lives and infrastructure. Hence, this study is aimed at achieving timely, anticipated and effective response throughout the cycle of a disaster, extreme weather and emergency operations management with the help of advanced technologies. This study proposes a novel, evidence-based framework (4-AIDE) that highlights the role of artificial intelligence (AI) and cloud-based collaborative platforms in disaster, extreme weather and emergency situations. A qualitative approach underpinned by organizational information processing theory (OIPT) is employed to design, develop and conduct semi-structured interviews with 33 respondents having experience in AI and cloud computing industries during emergency and extreme weather situations. For analysing the collected data, axial, open and selective coding is used that further develop themes, propositions and an evidence-based framework. The study findings indicate that AI and cloud-based collaborative platforms offer a structured and logical approach to enable two-way, algorithm-based communication to collect, analyse and design effective management strategies for disaster and extreme weather situations. Managers of public systems or businesses can collect and analyse data to predict possible outcomes and take necessary actions in an extreme weather situation. Communities and societies can be more resilient by transmitting and receiving data to AI and cloud-based collaborative platforms. These actions can also help policymakers identify critical pockets and guide administration for their necessary preparation for unexpected, extreme weather, and emergency events.
Recently, the fifth-generation (5G) communication network added the advancement in capability of network which increases Internet traffic in the world as well as increases the usage of Internet of Things (IoT) technologies. 5G technology brings revolution in the next generation of Industry 5.0 with the integration of the Internet of Things. The evolution of new 5G-enabled IoT increases the demand of cloud infrastructure and virtualization of resources in distributed networks. Artificial intelligence (AI) integrates intelligence with these IoT devices to learn from the given knowledge and make decisions independently. In recent years, AI has come up with large solutions set in every domain such as car automation, robotics, gaming, medical, image processing, information retrieval, smart data analysis, social media, digital marketing, and many more. Several researchers have proven already the application of AI in those fields and are still finding the application of AI. 5G is all about increasing speed and data rate so its biggest challenge is to identify the threats in between accurately. AI integration with 5G-enabled IoT is very essential. The integration of these technologies increases the generation of data day by day where the major concern is security of data which cannot be compromised at all. Risk, vulnerabilities, network exploration, traffic manipulation, and some other threats are instantiated to fool the system and existing static techniques. Adversarial attacks on machine learning model become hotspot in the current industry. In this chapter, an overview of adversarial AI techniques for IoT-enabled 5G network for classifying and detecting the threats automatically and securing transactions using blockchain are discussed.
In recent years, the Internet of Things (IoT) has made great progress. The interconnection between IoT and the Internet enables real-time information processing and transaction implementation through heterogeneous intelligent devices. But the security, the privacy, and the reliability of IoT are key challenges that limit its development. The features of blockchain, such as decentralization, consensus mechanism, data encryption, and smart contracts, are suitable for building distributed IoT systems to prevent potential attacks and to reduce transaction costs. As a decentralized and transparent database platform, blockchain has the potential to raise the performance of IoT security to a higher level. This paper systematically analyzes state-of-the-art of IoT security based on blockchain, paying special attention to the security features, issues, technologies, approaches, and related scenarios in blockchain-embedded IoT. The integration and interoperation of blockchain and IoT is an important and foreseeable development in computational communication system.
Cyber Threat Intelligence (CTI) can be used by organisations to assist their security teams in safeguarding their networks against cyber-attacks. This can be achieved by including threat data feeds into their networks or systems. However, despite being an effective Cyber Security (CS) tool, many organisations do not sufficiently utilise CTI. This is due to a number of reasons such as not fully understanding how to manage a daily flood of data filled with extraneous information across their security systems. This adds an additional layer of complexity to the tasks performed by their security teams who might not have the appropriate tools or sufficient skills to determine what information to prioritise and what information to disregard. Therefore, to help address the stated issue, this paper aims firstly to provide an in-depth understanding of what CTI is and how it can benefit organisations, and secondly to deliver a brief analysis of the application of Artificial Intelligence and Machine Learning in generating actionable CTI. The key contribution of this paper is that it assists organisations in better understanding their approach to CTI, which in turn will enable them to make informed decisions in relation to CTI.
The sixth generation (6G) networks are expected to provide a fully connected world with terrestrial wireless and satellite communications integration. The design concept of 6G networks is to leverage artificial intelligence (Ai) to promote the intelligent and agile development of network services. intelligent services inevitably involve the processing of large amounts of data, such as storage, computing, and analysis, such that the data may be vulnerable to tampering or contamination by attackers. in this article, we propose a blockchain-based data security scheme for Ai applications in 6G networks. Specifically, we first introduce the 6G architecture (i.e., a space-air-ground-underwater integrated network). Then we discuss two Ai-enabled applications, indoor positioning and autonomous vehicle, in the context of 6G. Through a case study of an indoor navigation system, we demonstrate the effectiveness of blockchain in data security. The integration of Ai and blockchain is developed to evaluate and optimize the quality of intelligent service. Finally, we discuss several open issues about data security in the upcoming 6G networks.
Blockchain technologies have grown in prominence in recent years, with many experts citing the potential applications of the technology in regard to different aspects of any industry, market, agency, or governmental organizations. In the brief history of blockchain, an incredible number of achievements have been made regarding how blockchain can be utilized and the impacts it might have on several industries. The sheer number and complexity of these aspects can make it difficult to address blockchain potentials and complexities, especially when trying to address its purpose and fitness for a specific task. In this survey, we provide a comprehensive review of applying blockchain as a service for applications within today’s information systems. The survey gives the reader a deeper perspective on how blockchain helps to secure and manage today information systems. The survey contains a comprehensive reporting on different instances of blockchain studies and applications proposed by the research community and their respective impacts on blockchain and its use across other applications or scenarios. Some of the most important findings this survey highlights include the fact that blockchain’s structure and modern cloud- and edge-computing paradigms are crucial in enabling a widespread adaption and development of blockchain technologies for new players in today unprecedented vibrant global market. Ensuring that blockchain is widely available through public and open-source code libraries and tools will help to ensure that the full potential of the technology is reached and that further developments can be made concerning the long-term goals of blockchain enthusiasts.
In recent years, unprecedented work has been done in the area of smart cities. The purpose of developing smart cities is to enhance quality of life factors for people dwelling within them. To achieve that purpose, technologies such as IoT and cloud computing have been utilized. Blockchain technology is also among the promising technologies that can offer countless valuable services to its end users. It is a immutable programmable digital register for the purpose of recording virtual assets having some value and was primarily developed for digital currencies like Bitcoin. To fully utilize the services of blockchain technology within smart cities, characteristics of blockchain technology, and its key requirements and research challenges need to be identified. Hence, in this article, an attempt has been made to identify the characteristics of blockchain technology. Furthermore, indispensable requirements for incorporating blockchain technology within smart cities are enumerated. A conceptual architecture for securing smart city using blockchain technology is proposed and explained using a possible use case study. An overview of a real-world three-blockchain- based smart city case study is also presented. Finally, several imperative research challenges are identified and discussed.
Experiments with new technologies in migration management are increasing. From Big Data predictions about population movements in the Mediterranean, to Canada's use of automated decision-making in immigration and refugee applications, to artificial-intelligence lie detectors deployed at European borders, States are keen to explore the use of new technologies, yet often fail to take into account profound human rights ramifications and real impacts on human lives. These technologies are largely unregulated, developed and deployed in opaque spaces with little oversight and accountability. This paper examines how technologies used in the management of migration impinge on human rights with little international regulation, arguing that this lack of regulation is deliberate, as States single out the migrant population as a viable testing ground for new technologies. Making migrants more trackable and intelligible justifies the use of more technology and data collection under the guise of national security, or even under tropes of humanitarianism and development. The way that technology operates is a useful lens that highlights State practices, democracy, notions of power, and accountability. Technology is not inherently democratic and human rights impacts are particularly important to consider in humanitarian and forced migration contexts. An international human rights law framework is particularly useful for codifying and recognising potential harms, because technology and its development is inherently global and transnational. More oversight and issue-specific accountability mechanisms are needed to safeguard fundamental rights of migrants such as freedom from discrimination, privacy rights and procedural justice safeguards such as the right to a fair decision-maker and the rights of appeal.
In the recent year, Internet of Things (IoT) is industrializing in several real-world applications such as smart transportation, smart city to make human life reliable. With the increasing industrialization inIoT, an excessive amount of sensing data is producing from various sensors devices in the Industrial IoT. To analyzes of big data, Artificial Intelligence (AI) plays a significant role as a strong analytic tool and delivers a scalable and accurate analysis of data in real-time. However, the design and development of a useful big data analysis tool using AI have some challenges, such as centralized architecture, security, and privacy, resource constraints, lack of enough training data. Conversely, asan emerging technology, Blockchain supports a decentralized architecture. It provides a secure sharingof data and resources to the various nodes of the IoT network is encouraged to remove centralizedcontrol and can overcome the existing challenges in AI. The main goal of our research is to designand develop an IoT architecture with blockchain and AI to support an effective big data analysis. Inthis paper, we propose a Blockchain-enabled Intelligent IoT Architecture with Artificial Intelligencethat provides an efficient way of converging blockchain and AI for IoT with current state-of-the-art techniques and applications. We evaluate the proposed architecture and categorized into twoparts: qualitative analysis and quantitative analysis. In qualitative evaluation, we describe how touse AI and Blockchain in IoT applications with ‘‘AI-driven Blockchain’’ and ‘‘Blockchain-driven AI.’’ Inquantitative analysis, we present a performance evaluation of the BlockIoTIntelligence architecture tocompare existing researches on device, fog, edge and cloud intelligence according to some parameterssuch as accuracy, latency, security and privacy, computational complexity and energy cost in IoT applications.TheevaluationresultsshowthattheproposedarchitectureperformanceovertheexistingIoT architectures and mitigate the current challenges.
Background
The protection of private data is a key responsibility for research studies that collect identifiable information from study participants. Limiting the scope of data collection and preventing secondary use of the data are effective strategies for managing these risks. An ideal framework for data collection would incorporate feature engineering, a process where secondary features are derived from sensitive raw data in a secure environment without a trusted third party.
Objective
This study aimed to compare current approaches based on how they maintain data privacy and the practicality of their implementations. These approaches include traditional approaches that rely on trusted third parties, and cryptographic, secure hardware, and blockchain-based techniques.
Methods
A set of properties were defined for evaluating each approach. A qualitative comparison was presented based on these properties. The evaluation of each approach was framed with a use case of sharing geolocation data for biomedical research.
Results
We found that approaches that rely on a trusted third party for preserving participant privacy do not provide sufficiently strong guarantees that sensitive data will not be exposed in modern data ecosystems. Cryptographic techniques incorporate strong privacy-preserving paradigms but are appropriate only for select use cases or are currently limited because of computational complexity. Blockchain smart contracts alone are insufficient to provide data privacy because transactional data are public. Trusted execution environments (TEEs) may have hardware vulnerabilities and lack visibility into how data are processed. Hybrid approaches combining blockchain and cryptographic techniques or blockchain and TEEs provide promising frameworks for privacy preservation. For reference, we provide a software implementation where users can privately share features of their geolocation data using the hybrid approach combining blockchain with TEEs as a supplement.
Conclusions
Blockchain technology and smart contracts enable the development of new privacy-preserving feature engineering methods by obviating dependence on trusted parties and providing immutable, auditable data processing workflows. The overlap between blockchain and cryptographic techniques or blockchain and secure hardware technologies are promising fields for addressing important data privacy needs. Hybrid blockchain and TEE frameworks currently provide practical tools for implementing experimental privacy-preserving applications.
This paper reviews the accounting literature that focuses on four Internet-related technologies that have the potential to dramatically change and disrupt the playing field for accountants and accounting researchers in the near future. These include cloud, big data, blockchain, and artificial intelligence (AI). For instance, access to distributed ledgers (blockchain) and big data supported by cloud-based analytics tools and AI will automate decision making to a large extent. These technologies may significantly improve financial visibility and allow more timely intervention due to the perpetual nature of accounting. However, given the number of tasks of which technology has relieved accountants, these technologies may also lead to concerns about the profession's legitimacy and the role of accountants. The findings suggest that scholars have not given sufficient attention to these technologies and how these technologies affect the everyday work of accountants. Research is urgently needed to understand the new kinds of accounting required to manage firms in the changing digital economy and to determine the new skills and competencies accountants may need to master to remain relevant and add value. The paper outlines a set of questions to guide future research.
As blockchain platforms are becoming increasingly noticeable in financial services and beyond, questions arise regarding their suitability to compete with or replace existing payment systems and marketplaces and redesign the financial infrastructures of the future. Prominent among these concerns are issues around governance and control in distributed ledgers: How are distributed ledger technologies governed? Can blockchains address complex administration problems? What key issues of note for practitioners and academics have emerged thus far? In this paper we aim to review the existing governance practices of established or popular blockchain and decentralized autonomous organization (DAO) systems with a view to understanding how they hold up in times of crises. What questions are raised when they are compromised or faced consensus challenges in coordinating action especially around control and accountability? We use a translational process, generating focal insights about present concerns from the reference point of completed academic studies and extensive practitioner consultation. Rather than adopting a declarative approach attempting to provide all the answers, we draw insights from the IT platform governance literature to offer a critical perspective for asking the right questions around key governance issues in financial infrastructure such as decision rights, control mechanisms, and incentives.
How the blockchain—a system built on foundations of mutual mistrust—can become trustworthy.
The blockchain entered the world on January 3, 2009, introducing an innovative new trust architecture: an environment in which users trust a system—for example, a shared ledger of information—without necessarily trusting any of its components. The cryptocurrency Bitcoin is the most famous implementation of the blockchain, but hundreds of other companies have been founded and billions of dollars invested in similar applications since Bitcoin's launch. Some see the blockchain as offering more opportunities for criminal behavior than benefits to society. In this book, Kevin Werbach shows how a technology resting on foundations of mutual mistrust can become trustworthy.
The blockchain, built on open software and decentralized foundations that allow anyone to participate, seems like a threat to any form of regulation. In fact, Werbach argues, law and the blockchain need each other. Blockchain systems that ignore law and governance are likely to fail, or to become outlaw technologies irrelevant to the mainstream economy. That, Werbach cautions, would be a tragic waste of potential. If, however, we recognize the blockchain as a kind of legal technology that shapes behavior in new ways, it can be harnessed to create tremendous business and social value.
Identifying attackers is a major apprehension to both organizations and governments. Recently, the most used applications for prevention or detection of attacks are intrusion detection systems. Biometrics technology is simply the measurement and use of the unique characteristics of living humans to distinguish them from one another and it is more useful as compare to passwords and tokens as they can be lost or stolen so we have choose the technique biometric authentication. The biometric authentication provides the ability to require more instances of authentication in such a quick and easy manner that users are not bothered by the additional requirements. In this paper, we have given a brief introduction about biometrics. Then we have given the information regarding the intrusion detection system and finally we have proposed a method which is based on fingerprint recognition which would allow us to detect more efficiently any abuse of the computer system that is running.
This Strategy Research Project takes a multi-faceted look at the island of Puerto Rico and defines its role in United States national security strategy. This paper presents facts on Puerto Rico's history, its people, geographical data and current events that support the author's thesis that Puerto Rico is an important link in the nation's security strategy. Comprehensive descriptions of military and other interagency forces stationed on the island and their missions are included in this composition. An analysis of the implications of discontinuing military training operations on the adjoining island of Vieques is included in the research project.