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There has been an increase in the usage of Internet of Things (IoT), which has recently become a rising area of interest as it is being extensively used for numerous applications and devices such as wireless sensors, medical devices, sensitive home sensors, and other related IoT devices. Due to the demand to rapidly release new IoT products in the...
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... much as IoT plays an important role in our current lives, it is evident that the use of IoT will play a critical part in the infrastructure of technology in the coming years [21]. According to a recent prediction, in 2025 the total number of connected devices in the world will approximately be 75.44 billion, Figure 2 [22]. While companies are racing to produce new IoT devices with creative applications, in many cases, unfortunately, security comes as an afterthought. ...
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... Researchers are exploring ways to improve security in IoT networks, and one promising solution is the use of Software Defined Networking (SDN). The authors in [71] present a system model that effectively combines SDN with IoT networks, mitigating man-in-themiddle attacks targeting IoT devices that use HTTP. The proposed system is implemented using Raspberry Pi, Kodi VOLUME 4, 2016 13 This article has been accepted for publication in IEEE Access. ...
The Internet of Things (IoT) has revolutionized both professional and personal spheres by enabling the widespread adoption of real-time applications and seamless data transmission over long distances. However, this rapid advancement presents significant challenges, particularly regarding security. IoT devices, often constrained by limited processing capabilities, struggle to implement robust security measures, underscoring the need to address these concerns within the IoT ecosystem. This paper conducts a comprehensive survey of the latest algorithms, techniques, and concepts in IoT, including novel algorithms that have been overlooked by previous studies. The selected literature is categorized based on performance, data security, data quality, and data transmission protocols, thereby identifying opportunities for future research. Additionally, this paper offers a bibliometric overview, providing comprehensive insights that aid researchers, engineers, and scientists in selecting suitable algorithms for specific applications and considering avenues for future improvements.
... This approach enables the centralized control and visibility of different IoT services to diverse users. To this end, in [23], a system model was introduced to optimize the integration of SDN with IoT networks, along with a strategy to counter man-in-the-middle threats targeting IoT devices. ...
Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment. Concurrently, the Internet of Things (IoT) connects numerous devices to the Internet, enabling autonomous interactions with minimal human intervention. However, implementing and managing an SDN-IoT system is inherently complex, particularly for those with limited resources, as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration. The findings of this study underscore the primary security and privacy challenges across application, control, and data planes. A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy protection. Recent investigations have explored cutting-edge methods, such as leveraging blockchain for transaction recording to enhance security and privacy, along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Moreover, the analysis indicates that encryption and hashing techniques are prevalent in the data plane, whereas access control and certificate authorization are prominently considered in the control plane, and authentication is commonly employed within the application plane. Additionally, this paper outlines future directions, offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem.
... This controller can also look back in time. Every component of the proposed system, 13 International Journal of Distributed Sensor Networks [93] proposed a solution to counteract man-in-the-middle attacks. They offered a system paradigm using SDN and the IoT. ...
Internet of things (IoT) and software-defined networking (SDN) are two relatively recent developments in the field of communication technology that have emerged in response to the growing demand for more efficient, flexible, and dynamic network architectures. As both of these concepts are new, they have received increasing attention from academic or industrial sources to emphasize their potential for integration. This study is aimed at reviewing the literature on SDN for IoT (SDN-IoT) published from 2014 to 2022 and presenting insights and directions for future research, with a particular focus on cloud, fog, and edge computing. The study collects data from Science Direct, IEEE Explore, and Google Scholar and objectively selects 126 papers and conducts metadata analysis. The study articulates the challenges of managing and orchestrating IoT systems and how SDN can be used to address these challenges by enabling dynamic and flexible network configurations. It delineates not only the function of blockchain (BC) technology in securing and managing IoT networks but also how SDN can be utilized to incorporate BC-based solutions. Additionally, the potential of SDN for mobile networks is explored, which are increasingly being used to support IoT devices. Finally, this study outlines the issues, challenges, and potential future research directions that may present opportunities for the researchers working in this field, underscoring the demand for more in-depth investigation and advancement.
... Effective data exploration methods for identifying "abnormal" and "normal" IoT components and behavior of devices inside the IoT ecosystem are DL and ML. Consequently, to transform the security of IoT systems from enabling secure Device-to-Device (D2D) connectivity to delivering intelligence security-based systems, ML/DL techniques are needed 33 . ...
The term “Internet of Things” (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent’s implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy.
... Also, many researchers have developed approaches to identify different types of attacks in the Application layer (SDN controller) of the SDN system to increase performance effectiveness [17]. Despite this, there has been minimal research on detecting unauthorized actions in the SDN system's control plane, a critical aspect for enhancing networking performance [18]. This article addresses the identification of malicious switches in the SDN system's data plane using a Deep Learning (DL) architecture [19]. ...
... Precision is intended to measure the detection process based on actual positives and predict the accurate positive scores to assess this forecast outcome. Additionally, the exact positive scores against the entire sample dataset, provided in Eq. (18), are used to evaluate precision. The validation of precision is detailed in Table 6. ...
In this paper, we design a Spider Monkey-based Elman Spike Neural Network (SM-ESNN) to identify intrusion threats in Software Defined Networks (SDN). Utilizing analysis of multidimensional Internet Protocol (IP) flows to find intrusion and flooding assaults against central controllers. Moreover, information is first gathered from the ISCXIDS2012 dataset and updated to the SDN's secure defensive system. The developed software defense system has two sub-modules: a detection module and a mitigation module. The developed technique's key benefit is improving SDN security by quickly and accurately identifying and stopping assaults. First, the proposed SM-ESNN method is implemented in Python. The assessment measures in this scenario include accuracy, specificity, sensitivity, precision, and false alarm rate (FAR). Furthermore, the suggested SM-ESNN approach obtained improved average performances of 98.24% accuracy, 97.34% specificity, 98.68% sensitivity, and 98.33% precision, which highlights its efficiency in detecting the attacks.
... Researchers need to take this into account so that users' privacy is protected as they use a unified platform across various devices. Security and privacy of network traffic are improved by SDN's fine-grained control of flows [57]. ...
Security networks as one of the biggest issue for network managers with the exponential growth of devices connected to the internet. Keeping a big and diverse network running smoothly and securely is no easy feat. With this in mind, emerging technologies like software defined networking (SDN) and internet of things (IoT) hold considerable promise for information service innovation in the cloud and big data era. Therefore, this paper describes the model of SDN and the architecture of IoT. Then this review does not only review the research studies in SDN-IoT but also provides an explanation of the SDN-IoT solution in terms of architecture, main consideration, model, and the implementation of SDN controllers for IoT. Finally, this review discusses the challenges and future directions. This paper can be used as a starting point for thinking about how to improve SDN-IoT security and privacy. This is an open access article under the CC BY-SA license.
... It is crucial to remember that IoT devices can be exploited in attacks against other devices or entities, in addition to assaults against devices directly. An assault model for wireless implantable medical devices is suggested, for example, in [13]. Methods for evaluating the safety of M2M/IoT communications are laid forth in [14]. ...
The IoT, or Internet of Things, has quickly grown in popularity as a means to collect data in real-time from any and all linked devices. These networked physical objects can exchange data with one another via their respective sensor technologies and have their own unique identifiers. Insightful data analytics applied to the obtained information also presents a substantial possibility for many organisations. Embedded devices, authentication, and trust management are all areas where the Internet of Things has shown a significant security hole. This study delves into the problems with the Internet of Things (IoT), covering topics such as its privacy and security, its vulnerability, its analytics at the moment, the impending ownership threat, trust management, IoT models, its roadmap, and its security issues. It then offers solutions to these problems.
... As data are gathered at centralized locations for ML model training, sensitive user information is potentially exposed. With data collected by connected edge devices expected to grow by over 75% by 2025 [1], and an estimated 79.4 zettabytes of data generated by 2025 with a 28.7% annual growth rate [2], conventional centralized ML models face significant challenges in sharing, storing, and processing such vast amounts of data while adhering to data protection regulations like the EU/ UK General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability (HIPAA) [3]. In response, federated learning (FL), a privacypreserving approach proposed by Google researchers [4], has gained substantial attention from industry and academia [5]. ...
Federated learning (FL) is a collaborative artificial intelligence (AI) approach that enables distributed training of AI models without data sharing, thereby promoting privacy by design. However, it is essential to acknowledge that FL only offers a partial solution to safeguard the confidentiality of AI and machine learning (ML) models. Unfortunately, many studies fail to report the results of privacy measurement when applying FL, mainly due to assumptions that privacy is implicitly achieved as FL is a privacy-by-design approach. This trend can also be attributed to the complexity of understanding privacy measurement metrics and methods. This paper presents a survey of privacy measurement in FL, aimed at evaluating its effectiveness in protecting the privacy of sensitive data during the training of AI and ML models. While FL is a promising approach for preserving privacy during model training, ensuring privacy is genuinely achieved in practice is crucial. By evaluating privacy measurement metrics and methods in FL, we can identify the gaps in existing approaches and propose new techniques to enhance FL’s privacy. A comprehensive study investigating “privacy measurement and metrics” in FL is therefore required to support the field’s growth. Our survey provides a critical analysis of the current state of privacy measurement in FL, identifies gaps in existing research, and offers insights into potential research directions. Moreover, this paper presents a case study that evaluates the effectiveness of various privacy techniques in a specific FL scenario. This case study serves as tangible evidence of the real-world implications of privacy measurements, providing insightful and practical guidelines for researchers and practitioners to optimize privacy preservation while balancing other crucial factors such as communication overhead and accuracy. Finally, our paper outlines a future roadmap for advancing privacy in FL, combining traditional techniques with innovative technologies such as quantum computing and Trusted Execution Environments to fortify data protection.
... Machine learning algorithms are crucial property rights for businesses in real-world application settings. Businesses will suffer significant damages if they are stolen [23]. ...
As artificial intelligence becomes more and more prevalent, machine learning algorithms are being used in a wider range of domains. Big data and processing power, which are typically gathered via crowdsourcing and acquired online, are essential for the effectiveness of machine learning. Sensitive and private data, such as ID numbers, personal mobile phone numbers, and medical records, are frequently included in the data acquired for machine learning training. A significant issue is how to effectively and cheaply protect sensitive private data. With this type of issue in mind, this article first discusses the privacy dilemma in machine learning and how it might be exploited before summarizing the features and techniques for protecting privacy in machine learning algorithms. Next, the combination of a network of convolutional neural networks and a different secure privacy approach is suggested to improve the accuracy of classification of the various algorithms that employ noise to safeguard privacy. This approach can acquire each layer's privacy budget of a neural network and completely incorporates the properties of Gaussian distribution and difference. Lastly, the Gaussian noise scale is set, and the sensitive information in the data is preserved by using the gradient value of a stochastic gradient descent technique. The experimental results showed that a balance of better accuracy of 99.05% between the accessibility and privacy protection of the training data set could be achieved by modifying the depth differential privacy model's parameters depending on variations in private information in the data.
... In this method, the controller assigns the unique session identifier to nodes in the network and the controller uses this identity of the node and the time difference between the request of authentication and reply to authentication. In [130], the researchers present an SDN-based secure IoT framework in which the communication of the data between the IoT devices is routed through the controller. The approach aims to prevent man-inthe-middle attacks. ...
... Better key management Scalability is difficult AVISPA security analyzer tool [124] End-to-end secure framework for Cloud application with use of index weight learning technique to train the indicators Application Layer Better performance due to self-learning approach Requires high computational time Implementation details not specified [125] Secure framework for home applications preventing the saturation attacks which result due to overload on the controllers Network Layer Prevents packet overflow at the forwarding devices High computational overhead POX controller, Simulator used is not specified [126] Role based controllers which uses separate controller for different roles Application Layer Low overhead on the controllers Large amount of data transfer between controllers Implementation details not specified [127] Authenticating patients with the help of MAC address of virtual machines and verifying at the SDN controller in healthcare sector Network Layer Availability of resources is well handled Faces Scalability issues POX controller and Mininet emulator [128] Lightweight cryptography algorithm for authenticating the smart home devices and preserving the privacy of data through cryptography between devices and SDN controller Network Layer Less computation time Faces Scalability issues Testbed: HTC One X, T1 MSP430 microcontroller and Intel Core i7-4510U laptop [129] A lightweight authentication protocol for authentication of smart home devices operating through SDN Network Layer Less computation time Works well for less number of devices, High overloads on the controller for a large number of devices ProVerif Simulator and Burrows Abadi-Needham (BAN) logic for testing [130] Communication of data from one device to another through SDN controller resulting in avoidance of man-in-the-middle attack Network Layer Easy to implement Heavy overload on the SDN controller and can result in high congestion Raspberry Pi and Kodi Media Center. Programs are written in C language devices to access the network resources in a secure manner by implementing the smart contacts at the edge layer rather than the IoT devices having resource constraints. ...
The Internet of Things (IoT) has revolutionized our society and become indispensable to modern existence. The IoT allows users to access their electronic gadgets from any location. The widespread adoption of IoT across sectors, from manufacturing to surveillance to elder care, has contributed to its rising profile. New security risks and challenges arise with the growth of the IoT. With the development of IoT, the likelihood of an attack by hackers has increased. The burden of addressing these dangers falls on researchers and security professionals. This article looks into the challenges of IoT security in a real-world Ambient Assisted Living (AAL) environment. This work discusses the numerous security attacks employed by cybercriminals in AAL IoT. In addition, this research investigates the varied responses to the risks. We discussed the state-of-the-art technologies available for protecting AAL IoT networks. This work analyses and compares the majority of the latest technologies available. In conclusion, we offer a few suggestions for where the field could go from the current scenario.