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All Outputs (33)

Assessing the Performance of Ethereum and Hyperledger Fabric Under DDoS Attacks for Cyber-Physical Systems (2024)
Presentation / Conference Contribution
Jayadev, V., Moradpoor, N., & Petrovski, A. (2024, July). Assessing the Performance of Ethereum and Hyperledger Fabric Under DDoS Attacks for Cyber-Physical Systems. Paper presented at 19th International Conference on Availability, Reliability and Security (ARES 2024), Vienna, Austria

Blockchain technology offers a decentralized and secure platform for addressing various challenges in smart cities and cyber-physical systems, including identity management, trust and transparency, and supply chain management. However, blockchains ar... Read More about Assessing the Performance of Ethereum and Hyperledger Fabric Under DDoS Attacks for Cyber-Physical Systems.

Enhancing Mac OS Malware Detection through Machine Learning and Mach-O File Analysis (2023)
Presentation / Conference Contribution
Thaeler, A., Yigit, Y., Maglaras, L. A., Buchanan, B., Moradpoor, N., & Russell, G. (2023, November). Enhancing Mac OS Malware Detection through Machine Learning and Mach-O File Analysis. Presented at IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMDAD) 2023, Edinburgh, UK

Malware research has predominantly focused on Windows and Android Operating Systems (OS), leaving Mac OS malware relatively unexplored. This paper addresses the growing threat of Mac OS malware by leveraging Machine Learning (ML) techniques. We propo... Read More about Enhancing Mac OS Malware Detection through Machine Learning and Mach-O File Analysis.

Reliability Analysis of Fault Tolerant Memory Systems (2023)
Presentation / Conference Contribution
Yigit, Y., Maglaras, L., Amine Ferrag, M., Moradpoor, N., & Lambropoulos, G. (2023, November). Reliability Analysis of Fault Tolerant Memory Systems. Presented at The 8th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM 2023), Piraeus, Greece

This paper delves into a comprehensive analysis of fault-tolerant memory systems, focusing on recovery techniques modelled using Markov chains to address transient errors. The study revolves around the application of scrubbing methods in conjunction... Read More about Reliability Analysis of Fault Tolerant Memory Systems.

The Threat of Adversarial Attacks Against Machine Learning-based Anomaly Detection Approach in a Clean Water Treatment System (2023)
Presentation / Conference Contribution
Moradpoor, N., Maglaras, L., Abah, E., & Robles-Durazno, A. (2023, June). The Threat of Adversarial Attacks Against Machine Learning-based Anomaly Detection Approach in a Clean Water Treatment System. Presented at 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT), Pafos, Cyprus

The protection of Critical National Infrastructure is extremely important due to nations being dependent on their operation and steadiness. Any disturbance to this infrastructure could have a devastating consequence on physical security, economic wel... Read More about The Threat of Adversarial Attacks Against Machine Learning-based Anomaly Detection Approach in a Clean Water Treatment System.

A Blockchain-based two Factor Honeytoken Authentication System (2023)
Presentation / Conference Contribution
Papaspirou, V., Maglaras, L., Kantzavelou, I., Moradpoor, N., & Katsikas, S. (2023, September). A Blockchain-based two Factor Honeytoken Authentication System. Poster presented at 28th European Symposium on Research in Computer Security (ESORICS), The Hague

This paper extends and advances our recently introduced two-factor Honeytoken authentication method by incorporating blockchain technology. This novel approach strengthens the authentication method, preventing various attacks, including tampering att... Read More about A Blockchain-based two Factor Honeytoken Authentication System.

Fake PLC in the cloud, we thought the attackers believed that: How ICS honeypot deception gets impacted by cloud deployments? (2023)
Presentation / Conference Contribution
Ivanova, S., & Moradpoor, N. (2023, April). Fake PLC in the cloud, we thought the attackers believed that: How ICS honeypot deception gets impacted by cloud deployments?. Presented at WFCS 2023: 19th IEEE International Conference on Factory Communication Systems, Pavia, Italy

The Industrial Control System (ICS) industry faces an ever-growing number of cyber threats - defence against which can be strengthened using honeypots. As the systems they mimic, ICS honeypots shall be deployed in a similar context to field ICS syste... Read More about Fake PLC in the cloud, we thought the attackers believed that: How ICS honeypot deception gets impacted by cloud deployments?.

Neutralising Adversarial Machine Learning in Industrial Control Systems Using Blockchain (2023)
Presentation / Conference Contribution
Moradpoor, N., Barati, M., Robles-Durazno, A., Abah, E., & McWhinnie, J. (2022, June). Neutralising Adversarial Machine Learning in Industrial Control Systems Using Blockchain. Presented at Cyber Science 2022: International Conference on Cybersecurity, Situational Awareness and Social Media, Cardiff Metropolitan University, Wales

The protection of critical national infrastructures such as drinking water, gas, and electricity is extremely important as nations are dependent on their operation and steadiness. However, despite the value of such utilities their security issues hav... Read More about Neutralising Adversarial Machine Learning in Industrial Control Systems Using Blockchain.

Deep Pre-trained Contrastive Self-Supervised Learning: A Cyberbullying Detection Approach with Augmented Datasets (2022)
Presentation / Conference Contribution
Alharigy, L. M., Al-Nuaim, H. A., & Moradpoor, N. (2022, December). Deep Pre-trained Contrastive Self-Supervised Learning: A Cyberbullying Detection Approach with Augmented Datasets. Presented at 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN), Al Khobar, Saudi Arabia

Cyberbullying is a widespread problem that has only increased in recent years due to the massive dependence on social media. Although, there are many approaches for detecting cyberbullying they still need to be improved upon for more accurate detecti... Read More about Deep Pre-trained Contrastive Self-Supervised Learning: A Cyberbullying Detection Approach with Augmented Datasets.

Using IOTA as an Inter-Vehicular Trust Mechanism in Autonomous Vehicles (2021)
Presentation / Conference Contribution
Cutajar, O., Moradpoor, N., & Jaroucheh, Z. (2021, December). Using IOTA as an Inter-Vehicular Trust Mechanism in Autonomous Vehicles. Presented at SINCONF 2021: 14th International Conference on Security of Information and Networks, Edinburgh (Online)

In a perfect world, coordination and cooperation across distributed autonomous systems would be a trivial task. However, incomplete information, malicious actors and real-world conditions can provide challenges which bring the trust-worthiness of par... Read More about Using IOTA as an Inter-Vehicular Trust Mechanism in Autonomous Vehicles.

VNWTS: A Virtual Water Chlorination Process for Cybersecurity Analysis of Industrial Control Systems (2021)
Presentation / Conference Contribution
Robles Durazno, A., Moradpoor, N., McWhinnie, J., & Porcel-Bustamante, J. (2021, December). VNWTS: A Virtual Water Chlorination Process for Cybersecurity Analysis of Industrial Control Systems. Presented at SINCONF 2021: 14th International Conference on Security of Information and Networks, Edinburgh (Online)

The rapid development of technology during the last decades has led to the integration of the network capabilities in the devices that are essential in the operation of Industrial Control Systems (ICS). Consequently, the attack surface of these asset... Read More about VNWTS: A Virtual Water Chlorination Process for Cybersecurity Analysis of Industrial Control Systems.

Real-time anomaly intrusion detection for a clean water supply system, utilising machine learning with novel energy-based features (2020)
Presentation / Conference Contribution
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., & Russell, G. (2020, July). Real-time anomaly intrusion detection for a clean water supply system, utilising machine learning with novel energy-based features. Presented at International Joint Conference on Neural Networks (IJCNN 2020), Glasgow, UK

Industrial Control Systems have become a priority domain for cybersecurity practitioners due to the number of cyber-attacks against those systems has increased over the past few years. This paper proposes a real-time anomaly intrusion detector for a... Read More about Real-time anomaly intrusion detection for a clean water supply system, utilising machine learning with novel energy-based features.

WaterLeakage: A Stealthy Malware for Data Exfiltration on Industrial Control Systems Using Visual Channels (2019)
Presentation / Conference Contribution
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., & Russell, G. (2019, July). WaterLeakage: A Stealthy Malware for Data Exfiltration on Industrial Control Systems Using Visual Channels. Presented at 15th IEEE International Conference on Control & Automation (ICCA), Edinburgh, Scotland

Industrial Control Systems (ICS) have faced a growing number of threats over the past few years. Reliance on isolated controls networks or air-gapped computers is no longer a feasible solution when it comes to protecting ICS. It is because the new ar... Read More about WaterLeakage: A Stealthy Malware for Data Exfiltration on Industrial Control Systems Using Visual Channels.

Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier (2019)
Presentation / Conference Contribution
Hall, A. J., Pitropakis, N., Buchanan, W. J., & Moradpoor, N. (2018, December). Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier. Presented at International Workshop on Big Data Analytics for Cyber Threat Hunting, Seattle, WA, USA

Insider threats continue to present a major challenge for the information security community. Despite constant research taking place in this area; a substantial gap still exists between the requirements of this community and the solutions that are cu... Read More about Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier.

Implementation and Detection of Novel Attacks to the PLC Memory on a Clean Water Supply System (2018)
Presentation / Conference Contribution
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., Russell, G., & Maneru-Marin, I. (2018, August). Implementation and Detection of Novel Attacks to the PLC Memory on a Clean Water Supply System. Presented at The 4th International Conference on Technology Trends, Babahoyo, Ecuador

Critical infrastructures such as nuclear plants or water supply systems are mainly managed through electronic control systems. Such systems comprise of a number of elements, such as programmable logic controllers (PLC), networking devices, and actua... Read More about Implementation and Detection of Novel Attacks to the PLC Memory on a Clean Water Supply System.

A supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system (2018)
Presentation / Conference Contribution
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., & Russell, G. (2018, June). A supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system. Presented at Cyber Security 2018: 2018 International Conference on Cyber Security and Protection of Digital Services, Glasgow, United Kingdom

Industrial Control Systems are part of our daily life in industries such as transportation, water, gas, oil, smart cities, and telecommunications. Technological development over time have improved their components including operating system platforms... Read More about A supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system.

Vulnerability Assessment of Objective Function of RPL Protocol for Internet of Things (2018)
Presentation / Conference Contribution
Semedo, F., Moradpoor, N., & Rafiq, M. (2018, September). Vulnerability Assessment of Objective Function of RPL Protocol for Internet of Things. Presented at 11th International Conference On Security Of Information and Networks, Cardiff, United Kingdom

The Internet of Things (IoT) can be described as the ever-growing global network of objects with built-in sensing and communication interfaces such as sensors, Global Positioning devices (GPS) and Local Area Network (LAN) interfaces. Security is by f... Read More about Vulnerability Assessment of Objective Function of RPL Protocol for Internet of Things.

Two Communities, One Topic: Exploring the British Reddit community split based on perceived biases (2018)
Presentation / Conference Contribution
Clavie, B., & Moradpoor, N. (2018, May). Two Communities, One Topic: Exploring the British Reddit community split based on perceived biases. Poster presented at 10th ACM Conference on Web Science, Amsterdam

This article explores a perceived bias between two British reddit communities dedicated to discussing British politics.We analyse the popular sources favoured by each community and study semantic indicators that would be indicative of a bias. Althoug... Read More about Two Communities, One Topic: Exploring the British Reddit community split based on perceived biases.

Employing machine learning techniques for detection and classification of phishing emails (2018)
Presentation / Conference Contribution
Moradpoor, N., Clavie, B., & Buchanan, B. (2017, July). Employing machine learning techniques for detection and classification of phishing emails. Presented at 2017 Computing Conference, London, UK

A phishing email is a legitimate-looking email which is designed to fool the recipient into believing that it is a genuine email, and either reveals sensitive information or downloads malicious software through clicking on malicious links contained i... Read More about Employing machine learning techniques for detection and classification of phishing emails.