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Dr. Nour Moustafa
University of New South Wales Canberra

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Research Keywords & Expertise

0 Cyber Security
0 Deep Learning
0 Intrusion Detection
0 Statistical Challenges
0 Privacy & Security

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Intrusion Detection
Deep Learning
Cyber Security
Privacy & Security

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Journal article
Published: 25 May 2021 in Sustainable Cities and Society
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The rapid proliferation of the Internet of Things (IoT) systems, has enabled transforming urban areas into smart cities. Smart cities’ paradigm has resulted in improved quality of life and better services to citizens, like smart healthcare, smart parking, smart transport, smart buildings, smart homes, and so on. One of the major challenges of IoT devices is the limited capacity of their battery because the devices consume a large amount of energy once they communicate with each other. Furthermore, the IoT-based smart systems would contain sensitive data about network systems, introducing serious privacy and security issues. IoT-based smart systems are highly exposed to botnet attacks. Examples of such attacks are Mirai and BASHLITE malware launched from compromised surveillance devices, which are common in smart cities, resulting in paralysis of Internet-based services through distributed denial of service (DDoS) attacks. Such DDoS attacks on IoT devices and their networks further threaten the emerging concept of sustainable smart cities. To discover such cyberattacks, this paper proposes a novel statistical learning-based botnet detection framework, called IoTBoT-IDS, which protects IoT-based smart networks against botnet attacks. IoTBoT-IDS captures the normal behavior of IoT networks by applying statistical learning-based techniques, using Beta Mixture Model (BMM) and a Correntropy model. Any deviation from the normal behavior is detected as an anomalous event. To evaluate IoTBoT-IDS, three benchmark datasets generated from realistic IoT networks were used. The evaluation results showed that IoTBoT-IDS effectively identifies various types of botnets with an average detection accuracy of 99.2%, which is higher by about 2–5% compared with compelling intrusion detection methods, namely AdaBoost ensemble learning, fuzzy c-means, and deep feed forward neural networks.

ACS Style

Javed Ashraf; Marwa Keshk; Nour Moustafa; Mohamed Abdel-Basset; Hasnat Khurshid; Asim D. Bakhshi; Reham R. Mostafa. IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities. Sustainable Cities and Society 2021, 72, 103041 .

AMA Style

Javed Ashraf, Marwa Keshk, Nour Moustafa, Mohamed Abdel-Basset, Hasnat Khurshid, Asim D. Bakhshi, Reham R. Mostafa. IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities. Sustainable Cities and Society. 2021; 72 ():103041.

Chicago/Turabian Style

Javed Ashraf; Marwa Keshk; Nour Moustafa; Mohamed Abdel-Basset; Hasnat Khurshid; Asim D. Bakhshi; Reham R. Mostafa. 2021. "IoTBoT-IDS: A novel statistical learning-enabled botnet detection framework for protecting networks of smart cities." Sustainable Cities and Society 72, no. : 103041.

Journal article
Published: 18 May 2021 in Mathematics
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One of the key challenges in cyber-physical systems (CPS) is the dynamic fitting of data sources under multivariate or mixture distribution models to determine abnormalities. Equations of the models have been statistically characterized as nonlinear and non-Gaussian ones, where data have high variations between normal and suspicious data distributions. To address nonlinear equations of these distributions, a cuckoo search algorithm is employed. In this paper, the cuckoo search algorithm is effectively improved with a novel strategy, known as a convergence speed strategy, to accelerate the convergence speed in the direction of the optimal solution for achieving better outcomes in a small number of iterations when solving systems of nonlinear equations. The proposed algorithm is named an improved cuckoo search algorithm (ICSA), which accelerates the convergence speed by improving the fitness values of function evaluations compared to the existing algorithms. To assess the efficacy of ICSA, 34 common nonlinear equations that fit the nature of cybersecurity models are adopted to show if ICSA can reach better outcomes with high convergence speed or not. ICSA has been compared with several well-known, well-established optimization algorithms, such as the slime mould optimizer, salp swarm, cuckoo search, marine predators, bat, and flower pollination algorithms. Experimental outcomes have revealed that ICSA is superior to the other in terms of the convergence speed and final accuracy, and this makes a promising alternative to the existing algorithm.

ACS Style

Mohamed Abdel-Basset; Reda Mohamed; Nazeeruddin Mohammad; Karam Sallam; Nour Moustafa. An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems. Mathematics 2021, 9, 1140 .

AMA Style

Mohamed Abdel-Basset, Reda Mohamed, Nazeeruddin Mohammad, Karam Sallam, Nour Moustafa. An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems. Mathematics. 2021; 9 (10):1140.

Chicago/Turabian Style

Mohamed Abdel-Basset; Reda Mohamed; Nazeeruddin Mohammad; Karam Sallam; Nour Moustafa. 2021. "An Adaptive Cuckoo Search-Based Optimization Model for Addressing Cyber-Physical Security Problems." Mathematics 9, no. 10: 1140.

Journal article
Published: 15 May 2021 in Sustainable Cities and Society
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While there has been a significant interest in understanding the cyber threat landscape of Internet of Things (IoT) networks, and the design of Artificial Intelligence (AI)-based security approaches, there is a lack of distributed architecture led to generating heterogeneous datasets that contain the actual behaviors of real-world IoT networks and complex cyber threat scenarios to evaluate the credibility of the new systems. This paper presents a novel testbed architecture of IoT network which can be used to evaluate Artificial Intelligence (AI)-based security applications. The platform NSX vCloud NFV was employed to facilitate the execution of Software-Defined Network (SDN), Network Function Virtualization (NFV) and Service Orchestration (SO) to offer dynamic testbed networks, which allow the interaction of edge, fog and cloud tiers. While deploying the architecture, real-world normal and attack scenarios are executed to collect labeled datasets. The generated datasets are named ‘TON_IoT’, as they comprise heterogeneous data sources collected from telemetry datasets of IoT services, Windows and Linux-based datasets, and datasets of network traffic. The TON_IoT network dataset is validated using four machine learning-based intrusion detection algorithms of Gradient Boosting Machine, Random Forest, Naive Bayes, and Deep Neural Networks, revealing a high performance of detection accuracy using the set of training and testing. A comparative summary of the TON_IoT network dataset and other competing network datasets demonstrates its diverse legitimate and anomalous patterns that can be used to better validate new AI-based security solutions. The architecture and datasets can be publicly accessed from TON_IOT Datasets (2020).

ACS Style

Nour Moustafa. A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets. Sustainable Cities and Society 2021, 72, 102994 .

AMA Style

Nour Moustafa. A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets. Sustainable Cities and Society. 2021; 72 ():102994.

Chicago/Turabian Style

Nour Moustafa. 2021. "A new distributed architecture for evaluating AI-based security systems at the edge: Network TON_IoT datasets." Sustainable Cities and Society 72, no. : 102994.

Journal article
Published: 16 March 2021 in Applied Soft Computing
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Deep Neural Network (DNN) models have vulnerabilities related to security concerns, with attackers usually employing complex hacking techniques to expose their structures. Data poisoning-enabled perturbation attacks are complex adversarial ones that inject false data into models. They negatively impact the learning process, with no benefit to deeper networks, as they degrade a model’s accuracy and convergence rates. In this paper, we propose an attack-agnostic-based defense method for mitigating their influence. In it, a Defensive Feature Layer (DFL) is integrated with a well-known DNN architecture which assists in neutralizing the effects of illegitimate perturbation samples in the feature space. To boost the robustness and trustworthiness of this method for correctly classifying attacked input samples, we regularize the hidden space of a trained model with a discriminative loss function called Polarized Contrastive Loss (PCL). It improves discrimination among samples in different classes and maintains the resemblance of those in the same class. Also, we integrate a DFL and PCL in a compact model for defending against data poisoning attacks. This method is trained and tested using the CIFAR-10 and MNIST datasets with data poisoning-enabled perturbation attacks, with the experimental results revealing its excellent performance compared with those of recent peer techniques.

ACS Style

Mohammed Hassanin; Ibrahim Radwan; Nour Moustafa; Murat Tahtali; Neeraj Kumar. Mitigating the impact of adversarial attacks in very deep networks. Applied Soft Computing 2021, 105, 107231 .

AMA Style

Mohammed Hassanin, Ibrahim Radwan, Nour Moustafa, Murat Tahtali, Neeraj Kumar. Mitigating the impact of adversarial attacks in very deep networks. Applied Soft Computing. 2021; 105 ():107231.

Chicago/Turabian Style

Mohammed Hassanin; Ibrahim Radwan; Nour Moustafa; Murat Tahtali; Neeraj Kumar. 2021. "Mitigating the impact of adversarial attacks in very deep networks." Applied Soft Computing 105, no. : 107231.

Journal article
Published: 22 February 2021 in IEEE Transactions on Professional Communication
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Brain signals are potential biometric markers in user authentication, complementing existing biometric authentication techniques (such as those based on fingerprint, iris and facial recognition). This paper proposes a novel EEG fusion method to examine the reliability and durability of EEG biometric markers across recording sessions. Our hypothesis is that models trained using EEG signals collected during various elicitation protocols can capture generalised brain patterns that pertain personalised information which can improve the durability of biometric systems. Different protocols are likely to produce different responses across brain regions, which can result in more identifiable responses from EEG. In our approach, an end-to-end convolutional neural network (CNN) model is adopted for feature extraction and classification of raw EEG data. The proposed method is evaluated on two EEG datasets which were collected over two separate sessions on different days using multiple different EEG elicitation protocols. Within-session and across-session experiments were conducted. Results for within session experiments showed that CNN models with protocol fusion can achieve similar if not better results than models trained with single protocol. In across-session scenarios, models trained with the proposed protocol fusion approach significantly outperformed single protocol based models. The obtained results illustrate the durability and reliability capabilities of the proposed fusion approach

ACS Style

Essam Debie; Nour Moustafa; Athanasios Vasilakos. Session Invariant EEG Signatures using Elicitation Protocol Fusion and Convolutional Neural Network. IEEE Transactions on Professional Communication 2021, PP, 1 -1.

AMA Style

Essam Debie, Nour Moustafa, Athanasios Vasilakos. Session Invariant EEG Signatures using Elicitation Protocol Fusion and Convolutional Neural Network. IEEE Transactions on Professional Communication. 2021; PP (99):1-1.

Chicago/Turabian Style

Essam Debie; Nour Moustafa; Athanasios Vasilakos. 2021. "Session Invariant EEG Signatures using Elicitation Protocol Fusion and Convolutional Neural Network." IEEE Transactions on Professional Communication PP, no. 99: 1-1.

Journal article
Published: 13 January 2021 in Future Generation Computer Systems
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There are various data management and security tools deployed at the cloud for storing and analyzing big data generated by the Internet of Things (IoT) and Industrial IoT (IIoT) systems. There is a recent trend to move such tools to edge networks (closer to the users and the IoT/IIoT systems) to address limitations, especially latency and security issues, in cloud-based solutions. However, protecting edge networks against zero-day attacks is challenging, due to the volume, variety and veracity of data collected from the large numbers of IoT devices in edge networks. In this paper, we propose a Distributed Anomaly Detection (DAD) system to discover zero-day attacks in edge networks. The proposed system uses Gaussian Mixture-based Correntropy, a novel ensemble one-class statistical learning model, which is designed to effectively monitor and recognize zero-day attacks in real-time from edge networks. We also design an IoT-edge-cloud architecture to illustrate the complexity of edge networks and how one can deploy the proposed system at network gateways. The proposed system is evaluated using both NSL-KDD and UNSW-NB15 datasets. The findings reveal that the proposed system achieves better performance, in terms of detection accuracy and processing time, compared with five anomaly detection techniques.

ACS Style

Nour Moustafa; Marwa Keshk; Kim-Kwang Raymond Choo; Timothy Lynar; Seyit Camtepe; Monica Whitty. DAD: A Distributed Anomaly Detection system using ensemble one-class statistical learning in edge networks. Future Generation Computer Systems 2021, 118, 240 -251.

AMA Style

Nour Moustafa, Marwa Keshk, Kim-Kwang Raymond Choo, Timothy Lynar, Seyit Camtepe, Monica Whitty. DAD: A Distributed Anomaly Detection system using ensemble one-class statistical learning in edge networks. Future Generation Computer Systems. 2021; 118 ():240-251.

Chicago/Turabian Style

Nour Moustafa; Marwa Keshk; Kim-Kwang Raymond Choo; Timothy Lynar; Seyit Camtepe; Monica Whitty. 2021. "DAD: A Distributed Anomaly Detection system using ensemble one-class statistical learning in edge networks." Future Generation Computer Systems 118, no. : 240-251.

Conference paper
Published: 27 November 2020 in Transactions on Petri Nets and Other Models of Concurrency XV
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The original version of this chapter was revised. The following corrections have been incorporated:

ACS Style

Waleed Yamany; Nour Moustafa; Benjamin Turnbull. Correction to: A Tri-level Programming Framework for Modelling Attacks and Defences in Cyber-Physical Systems. Transactions on Petri Nets and Other Models of Concurrency XV 2020, 12576, C1 -C1.

AMA Style

Waleed Yamany, Nour Moustafa, Benjamin Turnbull. Correction to: A Tri-level Programming Framework for Modelling Attacks and Defences in Cyber-Physical Systems. Transactions on Petri Nets and Other Models of Concurrency XV. 2020; 12576 ():C1-C1.

Chicago/Turabian Style

Waleed Yamany; Nour Moustafa; Benjamin Turnbull. 2020. "Correction to: A Tri-level Programming Framework for Modelling Attacks and Defences in Cyber-Physical Systems." Transactions on Petri Nets and Other Models of Concurrency XV 12576, no. : C1-C1.

Review
Published: 09 November 2020 in IEEE Access
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Advances in the Internet of Things (IoT) and aviation sector have resulted in the emergence of smart airports. Services and systems powered by the IoT enable smart airports to have enhanced robustness, efficiency and control, governed by real-time monitoring and analytics. Smart sensors control the environmental conditions inside the airport, automate passenger-related actions and support airport security. However, these augmentations and automation introduce security threats to network systems of smart airports. Cyber-attackers demonstrated the susceptibility of IoT systems and networks to Advanced Persistent Threats (APT), due to hardware constraints, software flaws or IoT misconfigurations. With the increasing complexity of attacks, it is imperative to safeguard IoT networks of smart airports and ensure reliability of services, as cyber-attacks can have tremendous consequences such as disrupting networks, cancelling travel, or stealing sensitive information. There is a need to adopt and develop new Artificial Intelligence (AI)-enabled cyber-defence techniques for smart airports, which will address the challenges brought about by the incorporation of IoT systems to the airport business processes, and the constantly evolving nature of contemporary cyber-attacks. In this study, we present a holistic review of existing smart airport applications and services enabled by IoT sensors and systems. Additionally, we investigate several types of cyber defence tools including AI and data mining techniques, and analyse their strengths and weaknesses in the context of smart airports. Furthermore, we provide a classification of smart airport sub-systems based on their purpose and criticality and address cyber threats that can affect the security of smart airport’s networks.

ACS Style

Nickolaos Koroniotis; Nour Moustafa; Francesco Schiliro; Praveen Gauravaram; Helge Janicke. A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports. IEEE Access 2020, 8, 209802 -209834.

AMA Style

Nickolaos Koroniotis, Nour Moustafa, Francesco Schiliro, Praveen Gauravaram, Helge Janicke. A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports. IEEE Access. 2020; 8 (99):209802-209834.

Chicago/Turabian Style

Nickolaos Koroniotis; Nour Moustafa; Francesco Schiliro; Praveen Gauravaram; Helge Janicke. 2020. "A Holistic Review of Cybersecurity and Reliability Perspectives in Smart Airports." IEEE Access 8, no. 99: 209802-209834.

Journal article
Published: 06 November 2020 in Electronics
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Supply chain 4.0 denotes the fourth revolution of supply chain management systems, integrating manufacturing operations with telecommunication and Information Technology processes. Although the overarching aim of supply chain 4.0 is the enhancement of production systems within supply chains, making use of global reach, increasing agility and emerging technology, with the ultimate goal of increasing efficiency, timeliness and profitability, Supply chain 4.0 suffers from unique and emerging operational and cyber risks. Supply chain 4.0 has a lack of semantic standards, poor interoperability, and a dearth of security in the operation of its manufacturing and Information Technology processes. The technologies that underpin supply chain 4.0 include blockchain, smart contracts, applications of Artificial Intelligence, cyber-physical systems, Internet of Things and Industrial Internet of Things. Each of these technologies, individually and combined, create cyber security issues that should be addressed. This paper explains the nature of the military supply chains 4.0 and how it uniquely differs from the commercial supply chain, revealing their strengths, weaknesses, dependencies and the fundamental technologies upon which they are built. This encompasses an assessment of the cyber risks and opportunities for research in the field, including consideration of connectivity, sensing and convergence of systems. Current and emerging semantic models related to the standardization, development and safety assurance considerations for implementing new technologies into military supply chains 4.0 are also discussed. This is examined from a holistic standpoint and through technology-specific lenses to determine current states and implications for future research directions.

ACS Style

Theresa Sobb; Benjamin Turnbull; Nour Moustafa. Supply Chain 4.0: A Survey of Cyber Security Challenges, Solutions and Future Directions. Electronics 2020, 9, 1864 .

AMA Style

Theresa Sobb, Benjamin Turnbull, Nour Moustafa. Supply Chain 4.0: A Survey of Cyber Security Challenges, Solutions and Future Directions. Electronics. 2020; 9 (11):1864.

Chicago/Turabian Style

Theresa Sobb; Benjamin Turnbull; Nour Moustafa. 2020. "Supply Chain 4.0: A Survey of Cyber Security Challenges, Solutions and Future Directions." Electronics 9, no. 11: 1864.

Journal article
Published: 20 October 2020 in IEEE Transactions on Network Science and Engineering
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With the prevalence of Internet of Things (IoT) systems, there should be a resilient connection between Space, Air, Ground, and Sea (SAGS) networks to offer automated services to end-users and organizations. However, such networks suffer from serious security and safety issues if IoT systems are not protected efficiently. Threat Intelligence (TI) has become a powerful security technique to understand cyber-attacks using artificial intelligence models that can automatically safeguard SAGS networks. In this paper, we propose a new TI scheme based on deep learning techniques that can discover cyber threats from SAGS networks. The proposed scheme contains three modules: a deep pattern extractor, TI-driven detection and TI-attack type identification technique. The deep pattern extractor module is designed to elicit hidden patterns of IoT networks, and its output used as input to the TI-driven detection. TI-attack type identification is used to identify the attack types of malicious patterns to assist in responding to security incidents. The proposed scheme is evaluated on the two datasets of TON-IoT and N-BAIOT. The experimental results prove that the scheme achieves high performances in terms of the detection and false alarm rates compared with other similar techniques.

ACS Style

Muna Al-Hawawreh; Nour Moustafa; Sahil Garg; M. Shamim Hossain. Deep Learning-enabled Threat Intelligence Scheme in the Internet of Things Networks. IEEE Transactions on Network Science and Engineering 2020, PP, 1 -1.

AMA Style

Muna Al-Hawawreh, Nour Moustafa, Sahil Garg, M. Shamim Hossain. Deep Learning-enabled Threat Intelligence Scheme in the Internet of Things Networks. IEEE Transactions on Network Science and Engineering. 2020; PP (99):1-1.

Chicago/Turabian Style

Muna Al-Hawawreh; Nour Moustafa; Sahil Garg; M. Shamim Hossain. 2020. "Deep Learning-enabled Threat Intelligence Scheme in the Internet of Things Networks." IEEE Transactions on Network Science and Engineering PP, no. 99: 1-1.

Journal article
Published: 20 October 2020 in IEEE Transactions on Cloud Computing
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In this paper, we first propose a multi-objective task-scheduling optimization problem that minimizes both the makespans and total costs in a fog-cloud environment. Then, we suggest an optimization model based on a Discrete Non-dominated Sorting Genetic Algorithm II (DNSGA-II) to deal with the discrete multi-objective task-scheduling problem and to automatically allocate tasks that should be executed either on fog or cloud nodes. The NSGA-II algorithm is adapted to discretize crossover and mutation evolutionary operators, rather than using continuous operators that require high computational resources and not able to allocate proper computing nodes. In our model, the communications between the fog and cloud tiers are formulated as a multi-objective function to optimize the execution of tasks. The proposed model allocates computing resources that would effectively run on either the fog or cloud nodes. Moreover, it efficiently organizes the distribution of workloads through various computing resources at the fog. Several experiments are conducted to determine the performance of the proposed model compared with a continuous NSGA-II (CNSGA-II) algorithm and four peer mechanisms. The outcomes demonstrate that the model is capable of achieving dynamic task scheduling with minimizing the total execution times (i.e. makespans) and costs in fog-cloud environments

ACS Style

Ismail M. Ali; Karam M. Sallam; Nour Moustafa; Ripon Chakraborty; Michael J. Ryan; Kim-Kwang Raymond Choo. An Automated Task Scheduling Model using Non-Dominated Sorting Genetic Algorithm II for Fog-Cloud Systems. IEEE Transactions on Cloud Computing 2020, PP, 1 -1.

AMA Style

Ismail M. Ali, Karam M. Sallam, Nour Moustafa, Ripon Chakraborty, Michael J. Ryan, Kim-Kwang Raymond Choo. An Automated Task Scheduling Model using Non-Dominated Sorting Genetic Algorithm II for Fog-Cloud Systems. IEEE Transactions on Cloud Computing. 2020; PP (99):1-1.

Chicago/Turabian Style

Ismail M. Ali; Karam M. Sallam; Nour Moustafa; Ripon Chakraborty; Michael J. Ryan; Kim-Kwang Raymond Choo. 2020. "An Automated Task Scheduling Model using Non-Dominated Sorting Genetic Algorithm II for Fog-Cloud Systems." IEEE Transactions on Cloud Computing PP, no. 99: 1-1.

Conference paper
Published: 25 September 2020 in Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond
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Drone systems, the so-called Unmanned Autonomous Vehicles (UAVs), have been widely employed in military and civilian sectors. Drone systems have been used for cyber warfare, warfighting and surveillance purposes of modern military and civilian applications. However, they have increasingly suffered from sophisticated malicious activities that exploit their vulnerabilities through network communications. As drones comprise a complex infrastructure as piloted aircraft but without operators, they still need a reliable security control to assert their safe operations. This paper proposes an autonomous intrusion detection scheme for discovering advanced and sophisticated cyberattacks that exploit drone networks. A testbed was configured to launch malicious events against a drone network for collecting legitimate and malicious observations and evaluate the performances of machine learning in real-time. Machine learning algorithms, including decision tree, k-nearest neighbors, naive Bayes, support vector machine and deep learning multi-layer perceptron, were trained and evaluated using the data collections, with promising results in terms of detection accuracy, false alarm rates, and processing times.

ACS Style

Nour Moustafa; Alireza Jolfaei. Autonomous detection of malicious events using machine learning models in drone networks. Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond 2020, 1 .

AMA Style

Nour Moustafa, Alireza Jolfaei. Autonomous detection of malicious events using machine learning models in drone networks. Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond. 2020; ():1.

Chicago/Turabian Style

Nour Moustafa; Alireza Jolfaei. 2020. "Autonomous detection of malicious events using machine learning models in drone networks." Proceedings of the 2nd ACM MobiCom Workshop on Drone Assisted Wireless Communications for 5G and Beyond , no. : 1.

Journal article
Published: 16 September 2020 in IEEE Transactions on Intelligent Transportation Systems
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Intelligent Transportation Systems (ITS), especially Autonomous Vehicles (AVs), are vulnerable to security and safety issues that threaten the lives of the people. Unlike manual vehicles, the security of communications and computing components of AVs can be compromised using advanced hacking techniques, thus barring AVs from the effective use in our routine lives. Once manual vehicles are connected to the Internet, called the Internet of Vehicles (IoVs), it would be exploited by cyber-attacks, like denial of service, sniffing, distributed denial of service, spoofing and replay attacks. In this article, we present a deep learning-based Intrusion Detection System (IDS) for ITS, in particular, to discover suspicious network activity of In-Vehicles Networks (IVN), vehicles to vehicles (V2V) communications and vehicles to infrastructure (V2I) networks. A Deep Learning architecture-based Long-Short Term Memory (LSTM) autoencoder algorithm is designed to recognize intrusive events from the central network gateways of AVs. The proposed IDS is evaluated using two benchmark datasets, i.e., the car hacking dataset for in-vehicle communications and the UNSW-NB15 dataset for external network communications. The experimental results demonstrated that our proposed system achieved over a 99% accuracy for detecting all types of attacks on the car hacking dataset and a 98% accuracy on the UNSW-NB15 dataset, outperforming other eight intrusion detection techniques.

ACS Style

Javed Ashraf; Asim D. Bakhshi; Nour Moustafa; Hasnat Khurshid; Abdullah Javed; Amin Beheshti. Novel Deep Learning-Enabled LSTM Autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems 2020, 22, 4507 -4518.

AMA Style

Javed Ashraf, Asim D. Bakhshi, Nour Moustafa, Hasnat Khurshid, Abdullah Javed, Amin Beheshti. Novel Deep Learning-Enabled LSTM Autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems. IEEE Transactions on Intelligent Transportation Systems. 2020; 22 (7):4507-4518.

Chicago/Turabian Style

Javed Ashraf; Asim D. Bakhshi; Nour Moustafa; Hasnat Khurshid; Abdullah Javed; Amin Beheshti. 2020. "Novel Deep Learning-Enabled LSTM Autoencoder Architecture for Discovering Anomalous Events From Intelligent Transportation Systems." IEEE Transactions on Intelligent Transportation Systems 22, no. 7: 4507-4518.

Journal article
Published: 09 September 2020 in IEEE Access
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Although the Internet of Things (IoT) can increase efficiency and productivity through intelligent and remote management, it also increases the risk of cyber-attacks. The potential threats to IoT applications and the need to reduce risk have recently become an interesting research topic. It is crucial that effective Intrusion Detection Systems (IDSs) tailored to IoT applications be developed. Such IDSs require an updated and representative IoT dataset for training and evaluation. However, there is a lack of benchmark IoT and IIoT datasets for assessing IDSs-enabled IoT systems. This paper addresses this issue and proposes a new data-driven IoT/IIoT dataset with the ground truth that incorporates a label feature indicating normal and attack classes, as well as a type feature indicating the sub-classes of attacks targeting IoT/IIoT applications for multi-classification problems. The proposed dataset, which is named TON_IoT, includes Telemetry data of IoT/IIoT services, as well as Operating Systems logs and Network traffic of IoT network, collected from a realistic representation of a medium-scale network at the Cyber Range and IoT Labs at the UNSW Canberra (Australia). This paper also describes the proposed dataset of the Telemetry data of IoT/IIoT services and their characteristics. TON_IoT has various advantages that are currently lacking in the state-of-the-art datasets: i) it has various normal and attack events for different IoT/IIoT services, and ii) it includes heterogeneous data sources. We evaluated the performance of several popular Machine Learning (ML) methods and a Deep Learning model in both binary and multi-class classification problems for intrusion detection purposes using the proposed Telemetry dataset.

ACS Style

Abdullah Alsaedi; Nour Moustafa; Zahir Tari; Abdun Mahmood; Adnan Anwar. TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems. IEEE Access 2020, 8, 165130 -165150.

AMA Style

Abdullah Alsaedi, Nour Moustafa, Zahir Tari, Abdun Mahmood, Adnan Anwar. TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems. IEEE Access. 2020; 8 (99):165130-165150.

Chicago/Turabian Style

Abdullah Alsaedi; Nour Moustafa; Zahir Tari; Abdun Mahmood; Adnan Anwar. 2020. "TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems." IEEE Access 8, no. 99: 165130-165150.

Research article
Published: 03 September 2020 in Transactions on Emerging Telecommunications Technologies
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Bio‐sensor data streaming and analytics is a key component of smart e‐healthcare. However, existing Internet of Things (IoT) ecosystem is unable to materialize the real‐time bio‐sensor data streaming and analytics within resource constrained environment. Moreover, traditional solutions fail to mitigate the edge‐cloud integration within a single sub‐system under IoT periphery which lead to investigate how edge‐cloud hybridization could be realized via similar set of tools. The objective of this article is to implement an integrated dual‐mode edge‐cloud system to serve streaming and analytics in real‐time. This study aims to achieve the aforesaid goal by presenting two different experiments that deals with the real‐time pulse sensor data streaming and analytics while utilizing light‐weight IoT‐supported JavaScript frameworks that includes Node.js, Johnny‐Five, Serialport.js, Plotly client, Flot.js, jQUERYy, Express Server, and Socket.io. Firstly, a standalone IoT‐edge system is developed and later, an integrated IoT‐based edge‐cloud system is developed to compare between the effectiveness of the systems. The implementation results show near correlation between the standalone edge and dual‐mode edge system. However, the dual‐mode edge‐cloud system provides more flexibility and capability to counter the bio‐sensor data streaming and analytics services within the constrained framework.

ACS Style

Partha Pratim Ray; Dinesh Dash; Nour Moustafa. Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective. Transactions on Emerging Telecommunications Technologies 2020, 31, 1 .

AMA Style

Partha Pratim Ray, Dinesh Dash, Nour Moustafa. Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective. Transactions on Emerging Telecommunications Technologies. 2020; 31 (11):1.

Chicago/Turabian Style

Partha Pratim Ray; Dinesh Dash; Nour Moustafa. 2020. "Streaming service provisioning in IoT‐based healthcare: An integrated edge‐cloud perspective." Transactions on Emerging Telecommunications Technologies 31, no. 11: 1.

Conference paper
Published: 25 August 2020 in Advances in Intelligent Systems and Computing
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Security solutions, especially intrusion detection and blockchain, have been individually employed in the cloud for detecting cyber threats and preserving private data. Both solutions demand ensembled models-based learning that can alert the campaign of complex malicious events and concurrently accomplish data privacy. Such models would also provide additional security and privacy to the live migration of Virtual Machines (VMs) in the cloud. This would allow the secure transfer of one or more VMs between datacentres or cloud providers in real-time. This paper proposes a Deep Blockchain Framework (DBF) designed to offer security-based distributed intrusion detection and privacy-based blockchain with smart contracts in the cloud. The intrusion detection method is employed yet using a Bidirectional Long Short-Term Memory (BiLSTM) deep learning algorithm to deal with sequential network data and is assessed using the dataset of UNSW-NB15. The Privacy-based blockchain and smart contract methods are developed using the Ethereum library to provide privacy to the distributed intrusion detection engines. The DBF framework is compared with compelling privacy-preserving intrusion detection models, and the empirical results reveal that DBF outperforms the compelling models. The framework has the potential to be used as a decision support system that can assist users and cloud providers in securely and timely migrating their data in a fast and reliable manner.

ACS Style

Osama Alkadi; Nour Moustafa; Benjamin Turnbull. A Collaborative Intrusion Detection System Using Deep Blockchain Framework for Securing Cloud Networks. Advances in Intelligent Systems and Computing 2020, 553 -565.

AMA Style

Osama Alkadi, Nour Moustafa, Benjamin Turnbull. A Collaborative Intrusion Detection System Using Deep Blockchain Framework for Securing Cloud Networks. Advances in Intelligent Systems and Computing. 2020; ():553-565.

Chicago/Turabian Style

Osama Alkadi; Nour Moustafa; Benjamin Turnbull. 2020. "A Collaborative Intrusion Detection System Using Deep Blockchain Framework for Securing Cloud Networks." Advances in Intelligent Systems and Computing , no. : 553-565.

Conference paper
Published: 25 August 2020 in Advances in Intelligent Systems and Computing
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Criminal Investigation (CI) plays an important role in policing, where police use various traditional techniques to investigate criminal activities such as robbery and assault. However, the techniques should hybrid with the use of artificial intelligence to analyze and determine different crime types for taking actions in real-time. In contrast with the manual process of investigating a large amount of data collected related to a criminal investigation. In this paper, we present a novel Cognitive Computing enabled Convolution Neural Networks (CC-CNN) approach for identifying crime types, such as robbery and assault, collected from unstructured textual data. We develop learning algorithms and provide a cognitive assistant to assist a police investigator in easily understanding crime types. We train and validate the CC-CNN technique on two datasets including handcrafted text-crime dataset and sentiment polarity dataset of negative and positive reviews. The experimental results show that our approach performs at a high level in terms of accuracy, error rate and time processing using both datasets.

ACS Style

Francesco Schiliro; Amin Beheshti; Nour Moustafa. A Novel Cognitive Computing Technique Using Convolutional Networks for Automating the Criminal Investigation Process in Policing. Advances in Intelligent Systems and Computing 2020, 528 -539.

AMA Style

Francesco Schiliro, Amin Beheshti, Nour Moustafa. A Novel Cognitive Computing Technique Using Convolutional Networks for Automating the Criminal Investigation Process in Policing. Advances in Intelligent Systems and Computing. 2020; ():528-539.

Chicago/Turabian Style

Francesco Schiliro; Amin Beheshti; Nour Moustafa. 2020. "A Novel Cognitive Computing Technique Using Convolutional Networks for Automating the Criminal Investigation Process in Policing." Advances in Intelligent Systems and Computing , no. : 528-539.

Book chapter
Published: 22 August 2020 in Encyclopedia of Wireless Networks
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ACS Style

Nour Moustafa; Jiankun Hu. Security and Privacy in 4G/LTE Network. Encyclopedia of Wireless Networks 2020, 1265 -1271.

AMA Style

Nour Moustafa, Jiankun Hu. Security and Privacy in 4G/LTE Network. Encyclopedia of Wireless Networks. 2020; ():1265-1271.

Chicago/Turabian Style

Nour Moustafa; Jiankun Hu. 2020. "Security and Privacy in 4G/LTE Network." Encyclopedia of Wireless Networks , no. : 1265-1271.

Journal article
Published: 10 August 2020 in Sustainability
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With the increasing popularity of the Internet of Things (IoT) platforms, the cyber security of these platforms is a highly active area of research. One key technology underpinning smart IoT systems is machine learning, which classifies and predicts events from large-scale data in IoT networks. Machine learning is susceptible to cyber attacks, particularly data poisoning attacks that inject false data when training machine learning models. Data poisoning attacks degrade the performances of machine learning models. It is an ongoing research challenge to develop trustworthy machine learning models resilient and sustainable against data poisoning attacks in IoT networks. We studied the effects of data poisoning attacks on machine learning models, including the gradient boosting machine, random forest, naive Bayes, and feed-forward deep learning, to determine the levels to which the models should be trusted and said to be reliable in real-world IoT settings. In the training phase, a label modification function is developed to manipulate legitimate input classes. The function is employed at data poisoning rates of 5%, 10%, 20%, and 30% that allow the comparison of the poisoned models and display their performance degradations. The machine learning models have been evaluated using the ToN_IoT and UNSW NB-15 datasets, as they include a wide variety of recent legitimate and attack vectors. The experimental results revealed that the models’ performances will be degraded, in terms of accuracy and detection rates, if the number of the trained normal observations is not significantly larger than the poisoned data. At the rate of data poisoning of 30% or greater on input data, machine learning performances are significantly degraded.

ACS Style

Corey Dunn; Nour Moustafa; Benjamin Turnbull. Robustness Evaluations of Sustainable Machine Learning Models Against Data Poisoning Attacks in the Internet of Things. Sustainability 2020, 12, 6434 .

AMA Style

Corey Dunn, Nour Moustafa, Benjamin Turnbull. Robustness Evaluations of Sustainable Machine Learning Models Against Data Poisoning Attacks in the Internet of Things. Sustainability. 2020; 12 (16):6434.

Chicago/Turabian Style

Corey Dunn; Nour Moustafa; Benjamin Turnbull. 2020. "Robustness Evaluations of Sustainable Machine Learning Models Against Data Poisoning Attacks in the Internet of Things." Sustainability 12, no. 16: 6434.

Review
Published: 20 July 2020 in Electronics
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The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

ACS Style

Javed Asharf; Nour Moustafa; Hasnat Khurshid; Essam Debie; Waqas Haider; Abdul Wahab. A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions. Electronics 2020, 9, 1177 .

AMA Style

Javed Asharf, Nour Moustafa, Hasnat Khurshid, Essam Debie, Waqas Haider, Abdul Wahab. A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions. Electronics. 2020; 9 (7):1177.

Chicago/Turabian Style

Javed Asharf; Nour Moustafa; Hasnat Khurshid; Essam Debie; Waqas Haider; Abdul Wahab. 2020. "A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions." Electronics 9, no. 7: 1177.