Nour Moustafa

Nour Moustafa
UNSW Canberra · SEIT

PhD in Computer Science (Senior Lecturer in Cybersecurity @UNSW Canberra)

About

176
Publications
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Publications

Publications (176)
Article
Full-text available
Abstract--- The prevalence of interconnected appliances and ubiquitous computing face serious threats from the hostile activities of network attackers. Conventional Intrusion Detection Systems (IDSs) are incapable of detecting these intrusive events as their outcomes reflect high false positive rates (FPRs). In this paper, we present a novel Geometr...
Chapter
Full-text available
An intrusion detection system has become a vital mechanism to detect a wide variety of malicious activities in the cyber domain. However, this system still faces an important limitation when it comes to detecting zero-day attacks, concerning the reduction of relatively high false alarm rates. I is thus necessary to no longer consider the tasks of m...
Article
Full-text available
Over the last three decades, Network Intrusion Detection Systems (NIDSs), particularly, Anomaly Detection Systems (ADSs), have become more significant in detecting novel attacks than Signature Detection Systems (SDSs). Evaluating NIDSs using the existing benchmark data sets of KDD99 and NSLKDD does not reflect satisfactory results, due to three maj...
Conference Paper
Full-text available
One of the major research challenges in this field is the unavailability of a comprehensive network based data set which can reflect modern network traffic scenarios, vast varieties of low footprint intrusions and depth structured information about the network traffic. Evaluating network intrusion detection systems research efforts, KDD98, KDDCUP99...
Conference Paper
Full-text available
Because of the increase flow of network traffic and its significance to the provision of ubiquitous services, cyberattacks attempt to compromise the security principles of confidentiality, integrity and availability. A Network Intrusion Detection System (NIDS) monitors and detects cyber-attack patterns over networking environments. Network packets...
Article
The Internet of Things (IoT) ecosystem connects physical devices to the internet, offering significant advantages in agility, responsiveness and potential environmental benefits. The number and variety of IoT devices are sharply increasing, and as they do, they generate significant data sources. Deep learning algorithms are increasingly integrated...
Article
A large number of network security breaches in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network interruptions and loss of sensitive data have occurred, which led to an active research area for improving NIDS technologies. In an analysis of related works, it was observed t...
Article
Federated learning methods offer secured monitor services and privacy-preserving paradigms to end-users and organisations in the Internet of Things networks such as smart healthcare systems. Federated learning has been coined to safeguard sensitive data, and its global aggregation is often based on a centralised server. This design is vulnerable to...
Article
The unwanted vehicular engine irregularities diminish vehicular competence, hinder productivity, waste time, and sluggish personal/national economic growth. Transportation sectors are essential infrastructures that require practical vulnerability assessment to avoid unexpected consequences through risk severity assessment. Artificial intelligence w...
Article
Federated Learning (FL), as an emerging form of distributed machine learning, can protect participants’ private data from being substantially disclosed to cyber adversaries. It has potential uses in many large-scale, data-rich environments, such as the Internet of Things (IoT), Industrial IoT, Social Media, and the emerging SM 3.0. However, federat...
Article
Full-text available
Cyber-physical systems (CPS) and their Supervisory Control and Data Acquisition (SCADA) have attracted great interest for automatic management of industrial infrastructures, such as water and wastewater systems. A range of technologies can be employed for wastewater treatment CPS to manage risks and protect the infrastructures of water systems and...
Article
Full-text available
Cyber-physical systems (CPS) generate big data collected from combining physical and digital entities, but the challenge of CPS privacy-preservation demands further research to protect CPS sensitive information from unauthorized access. Data mining, perturbation, transformation and encryption are techniques extensively used to preserve private info...
Article
By combining the Internet of Things (IoT) and Artificial Intelligence (AI), new augmentations and enhancements are realized, resulting in environment-aware systems that can enable intelligent decision making, with one such example being smart satellite networks. However, smart satellite networks attract the attention of hackers, resulting in them b...
Article
Healthcare applications demand systematic approaches to eradicate inevitable human errors to design a framework that systematically eliminates cyber-threats. The key focus of this paper is to provide a comprehensive survey on the use of modern enabling technologies, such as the Internet of Things (IoT), 5G networks, artificial intelligence (AI), an...
Article
Federated Learning (FL) with mobile computing and Internet of Things (IoT) is an effective cooperative learning approach. However, several technical challenges still need to be addressed. For instance, dividing the training process among several devices may impact the performance of Machine Learning (ML) algorithms, often significantly degrading pr...
Article
Learning discriminative features with adversarial behaviors can be extremely challenging to build a robust learning model. This is partly evidenced by the difficulties in training robust maximum-margin models (e.g., ArcFace and CosFace) that cannot discriminate decision boundaries between perturbed samples perfectly. One potential approach is to de...
Article
With the rapid evolution of web technologies, Web 3.0 aims to expand on current and emerging social media platforms such as Facebook, Twitter, and TikTok, and integrate emerging computing paradigms, including the Internet of Things (IoT), named social media 3.0. The combinations of these platforms in Web 3.0 promises consumers greater integration,...
Article
Full-text available
COVID-19 has severely disrupted every aspect of society and left negative impact on our life. Resisting the temptation in engaging face-to-face social connection is not as easy as we imagine. Breaking ties within social circle makes us lonely and isolated, that in turns increase the likelihood of depression related disease and even can leads to dea...
Article
The dependency on vehicles is increasing tremendously due to its excellent transport capacity, fast, efficient, flexible, pleasant journey, minimal physical effort, and substantial economic impact. As a result, the demand for smart and intelligent feature enhancement is growing and becoming a prime concern for maximum productivity based on the curr...
Chapter
Previous chapters demonstrate the great success achieved by principally in supervised settings, by leveraging a larger volume of precisely annotated dataset. Nevertheless, annotated data instances are frequently complicated, costly, or laborious to acquire.
Chapter
This chapter elaborates on the potential of supervised deep learning solutions for fulfilling the security requirements of IoT-based systems with main aim to realize a reliable and trustworthy IoT environment.
Chapter
This chapter mainly presents a detailed discussion of the IoT technologies and dependent systems with the main objectives of emphasizing the main attributes of IoT systems that might possibly threaten the security of the system. Firstly, the definition and of the IoT system and the detailed description of its architecture are presented along with a...
Chapter
This chapter elaborates on the potential of unsupervised deep learning solutions for assuring the security of IoT-based systems to give the reader an insightful discussion of how these solutions could satisfy the IoT security requirements necessary to realize a reliable and trustworthy IoT environment.
Chapter
The Internet of Things (IoT) is a rapidly evolving technology that empowers billions of globally distributed physical things to be interconnected over the internet to capture, collect, exchange, and share a wide variety of vast amounts of data. These physical things incorporate all of the connectable devices ranging from conventional household devi...
Chapter
Reinforcement learning (RL) is identified as a branch of artificial intelligence (AI) the seek to addresses the dilemma of automated learning of ideal determinations throughout time, which is a popular and broad challenge explored in lots of technical and industrial disciplines. RL nativey integrates an additional dimension (which is typically time...
Chapter
The rapid evolution of the Internet of Things (IoT) and relevant applications have been paving the way toward the fulfillment of smart cities. Smart cities are thought to come up with multiple crucial smart IoT applications i.e., smart manufacturing, smart transportation, autonomous driving/flight, smart buildings, smart healthcare, smart grid, and...
Chapter
This chapter elaborates on different security aspects to be taken into accounts during the development and the deployments of IoT architecture. To make the reader about the security of the IoT based system, this chapter begins by defining the contemporary security requirements that should be considered to realize a reliable and trustworthy IoT envi...
Chapter
As IoT technology becomes an integral part of everyday life, enhancing productivity for businesses through automation, it should come as no surprise that attackers would seek to exploit these systems and the services they provide for profit.
Chapter
The central intention of this chapter is to discuss the primary security challenges in internet of things (IoT) environments with the main emphasis on the opportunities for deep learning for securing and maintaining the privacy of IoT-based systems.
Book
This book states that the major aim audience are people who have some familiarity with Internet of things (IoT) but interested to get a comprehensive interpretation of the role of deep Learning in maintaining the security and privacy of IoT. A reader should be friendly with Python and the basics of machine learning and deep learning. Interpretation...
Article
Full-text available
Cyber-Physical Systems (CPSs) enable Information Technology to be integrated with Operation Technology to efficiently monitor and manage the physical processes of various critical infrastructures. Recent incidents in cyber ecosystems have shown that CPSs are becoming increasingly vulnerable to complex attacks. These incidents often lead to sensing...
Article
Full-text available
Video watermarking techniques can be used to prevent unauthorized users from illegally distributing videos across (social) media networks. However, current watermarking solutions are unable to embed a perceptually invisible watermark which is robust to the distortions introduced by camcording. These watermark-disrupting distortions include lossy co...
Article
While recent works on investigating renewable energy sources for powering the highway offer promising solutions for sustainable environments, they are often impeded by unequal distribution of sources across the region due to variations in solar exposure and road intensity that electromagnetically and mechanically generate the energy. By exploiting...
Article
Social media (SM) 3.0 integrates SM platforms, such as Facebook and Twitter, with the Internet of Things (IoT), and has a great potential to change how we interact with mobile devices, online platforms, and the world around us. This integration with end users produces large-scale and heterogeneous data sources that demand machine learning (ML)-base...
Article
Intelligent transportation systems, especially Autonomous Vehicles (AVs), are emerging as a paradigm with the potential to change modern society. However, with this, there is a strong need to ensure the security and privacy of such systems. AV ecosystems depend on machine learning algorithms to autonomously control their operations. Given the amoun...
Article
According to statistics, in the 185 countries' 36 types of cancer, the morbidity and mortality of lung cancer take the first place, and non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer [1-2]. Significantly in many developing countries, limited medical resources and excess population seriously affect the diagnosis and treatment of...
Article
The Internet of Medical Things (IoMT) is structured upon both the sensing and communication infrastructure and computation facilities. The IoMT provides the convenient and cheapest ways for healthcare by aiding the remote access to the patients’ physiological data and using machine learning techniques for help in diagnosis. The communication delays...
Article
Industrial Internet of Things (IIoT) and Industry 4.0 empower interrelation among manufacturing processes, industrial machines, and utility services. The time-critical data collected from heterogeneous sensing devices are usually communicated to processing points for analysis and aggregation as the basis of IIoT. The IIoTs’ service quality typicall...
Article
The lack of a gold standard synergy quantification method for chemotherapeutic drug combinations warrants the consideration of different synergy metrics to develop efficient predictive models. Furthermore, neglecting combination sensitivity may lead to biased synergistic combinations, which are ineffective in cancer treatment. In this paper, we pro...
Preprint
Full-text available
The uses of Machine Learning (ML) in detection of network attacks have been effective when designed and evaluated in a single organisation. However, it has been very challenging to design an ML-based detection system by utilising heterogeneous network data samples originating from several sources. This is mainly due to privacy concerns and the lack...
Article
The modern industrial sector generates enormous amounts of high‐dimensional heterogeneous data daily. However, mostly the vectored data (rank‐one tensor) have been considered for anomaly detection, whereas the data in real‐life is high dimensional. The expressive power of methods based on vector data is restrictive as they may destroy the structura...
Article
With the integration of the Internet of Things (IoT) in the field of transportation, the Internet of Vehicles (IoV) turned to be a vital method for designing Smart Transportation Systems (STS). STS consist of various interconnected vehicles and transportation infrastructure exposed to cyber intrusion due to the broad usage of software and the initi...
Article
Recognizing human activities is considered a vital research challenge because of its essential significance for improving human-machine collaboration in the Internet of things environments. The present deep learning (DL) literature focused on studying HAs from one subject, with several schemes differing in the recognition method, and sensing strate...
Article
The Industrial Internet of Things (IIoT) is creating a massive impact in a wide range of applications. In addition, with the forthcoming 5G and 6G technologies, vehicular ad-hoc networks will have pioneer advancements. However, security concerns are not well addressed, as vehicular networks should be deployed at a large scale. To address the securi...
Article
The Internet of Vehicular Things (IoVT) is turning into an indubitably evolving area of interest in either industrial or academic domains. The tremendous information exchanging between IoVT devices enable the development of a wide variety of vehicular applications i.e., intelligent transport systems (ITS) and autonomous driving system (ADS), etc. H...
Article
COVID-19 stay threatening the health infrastructure worldwide. Computed tomography (CT) was demonstrated as an informative tool for the recognition, quantification, and diagnosis of this kind of disease. It is urgent to design efficient deep learning (DL) approach to automatically localize and discriminate COVID-19 from other comparable pneumonia o...
Conference Paper
With the rapid increase in smart devices and lowering prices of sensing devices, adoption of the Internet of Things (IoT) is gaining impetus. These IoT devices come with a greater threat of being attacked or compromised that could lead to the Denial of Service (DoS) and Distributed Denial of Service (DDoS). The high volume of IoT devices with a hig...
Preprint
Full-text available
The Internet of Things (IoT) paradigm has displayed tremendous growth in recent years, resulting in innovations like Industry 4.0 and smart environments that provide improvements to efficiency, management of assets and facilitate intelligent decision making. However, these benefits are offset by considerable cybersecurity concerns that arise due to...
Article
This Special Section on "AI-Enabled Threat Intelligence and Hunting Microservices for Distributed Industrial IoT System" highlights the main research challenges in the AI-based security and privacy Microservices for IIoT systems. Of the 39 submissions, seven papers were eventually accepted after undergoing several rounds of rigorous peer reviews (i...
Article
Full-text available
Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightweight protocols, such as constrained application protocol (CoAP), have been widely utilised in transferring sensing data of medical devices t...
Article
Full-text available
Despite the remarkable work conducted to improve fog computing applications’ efficiency, the task scheduling problem in such an environment is still a big challenge. Optimizing the task scheduling in these applications, i.e. critical healthcare applications, smart cities, and transportation is urgent to save energy, improve the quality of service,...
Article
The management of contemporary communication networks of Supply chain (SC) 4.0 is becoming more complex due to the heterogeneity requirements of new devices concerning the integration of the Internet of Things (IoT) in the legacy industry networks, such as Cyber-Physical Systems (CPS). Hence, in this case, it becomes a challenging task to secure ne...
Article
Mobile Crowdsensing (MCS) is a cost-efficient community sensing paradigm. It employs massive mobile computing devices such as smartphones to sense and propagate data collectively. Two main problems in MCS aiming to use these applications efficiently and their generated data are participants selection problem and incentive mechanism design problem....
Article
The Internet of Medical Things (IoMT) is increasingly replacing the traditional healthcare systems. However, less focus has been paid to their security against cyber-threats in the implementation of the IoMT and its networks. One of the key reasons can be the challenging task of optimizing typical security solutions to the IoMT networks. And despit...
Article
Differential privacy (DP) remains a potent solution to what is arguably the defining issue in machine learning: balancing user privacy with an ever-increasing need for data. Practitioners must respect privacy, especially in sensitive healthcare domains. DP strives towards this aim by adding noise to training data to occlude its origin and nature, a...
Article
Rapid technological advancements have resulted in increasingly more efficient and lightweight devices that, coupled with low-power and wide-range wireless connectivity, have given rise to Industrial Internet of Things (IIoT) systems. As a result, the concept of intelligent environments was developed, such as smart airports, where ubiquitous sensors...
Article
Machine Learning (ML) algorithms can effectively perform analytics and inferences for building smart applications, such as early detection of diseases in the Industrial Internet of Things (IIoT) and smart healthcare systems. The main components of ML, including training and testing phases, can be decomposed into microservices to improve service qua...
Preprint
Full-text available
The tremendous numbers of network security breaches that have occurred in IoT networks have demonstrated the unreliability of current Network Intrusion Detection Systems (NIDSs). Consequently, network interruptions and loss of sensitive data have occurred which led to an active research area for improving NIDS technologies. During an analysis of re...
Article
Intelligent Transportation Systems (ITS), particularly Autonomous Vehicles (AVs), are susceptible to safety and security concerns that impend people's lives. Nothing like manually controlled vehicles, the safekeeping of communications and computing constituents of AVs can be threatened using sophisticated hacking techniques, consequently disrupting...
Article
Advanced IIoT systems, can be used to facilitate smart management. Nevertheless, IIoT systems generates huge amounts of data that need to be outsourced to the cloud for storing and providing real-time search facilities to end-users. Outsourcing IIoT data to a third-party cloud service provider (CSP) introduce several data privacy and integrity issu...
Article
Full-text available
Board games have often been recognised as a tool to model complex concepts in abstract environments for entertainment, education, and research in fields such as military and artificial intelligence. With more board games being designed and published, it is timely to draw attention towards board game design strategies and mechanics which capture the...
Article
Full-text available
The Internet of Things (IoT) is reshaping our connected world as the number of lightweight devices connected to the Internet is rapidly growing. Therefore, high-quality research on intrusion detection in the IoT domain is essential. To this end, network intrusion datasets are fundamental, as many attack detection strategies have to be trained and e...
Preprint
Machine learning (ML)-based network intrusion detection system (NIDS) plays a critical role in discovering unknown and novel threats in a large-scale cyberspace. It has been widely adopted as a mainstream hunting method in many organizations, such as financial institutes, manufacturing companies and government agencies. However, there are two chall...