Outputs (32)

Neutralising Adversarial Machine Learning in Industrial Control Systems Using Blockchain (2023)
Conference Proceeding
Moradpoor, N., Barati, M., Robles-Durazno, A., Abah, E., & McWhinnie, J. (2023). Neutralising Adversarial Machine Learning in Industrial Control Systems Using Blockchain. In Proceedings of the International Conference on Cybersecurity, Situational Awareness and Social Media (437-451). https://doi.org/10.1007/978-981-19-6414-5_24

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)
Conference Proceeding
Alharigy, L. M., Al-Nuaim, H. A., & Moradpoor, N. (2022). Deep Pre-trained Contrastive Self-Supervised Learning: A Cyberbullying Detection Approach with Augmented Datasets. In 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN). https://doi.org/10.1109/CICN56167.2022.10008274

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.

Building Towards Automated Cyberbullying Detection: A Comparative Analysis (2022)
Journal Article
Al Harigy, L. M., Al Nuaim, H. A., Moradpoor, N., & Tan, Z. (2022). Building Towards Automated Cyberbullying Detection: A Comparative Analysis. Computational Intelligence and Neuroscience, 2022, Article 4794227. https://doi.org/10.1155/2022/4794227

The increased use of social media between digitally anonymous users, sharing their thoughts and opinions, can facilitate participation and collaboration. However, it’s this anonymity feature which gives users freedom of speech and allows them to cond... Read More about Building Towards Automated Cyberbullying Detection: A Comparative Analysis.

VNWTS: A Virtual Water Chlorination Process for Cybersecurity Analysis of Industrial Control Systems (2021)
Conference Proceeding
Robles Durazno, A., Moradpoor, N., McWhinnie, J., & Porcel-Bustamante, J. (2021). VNWTS: A Virtual Water Chlorination Process for Cybersecurity Analysis of Industrial Control Systems. In 2021 14th International Conference on Security of Information and Networks (SIN). https://doi.org/10.1109/SIN54109.2021.9699375

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.

Using IOTA as an Inter-Vehicular Trust Mechanism in Autonomous Vehicles (2021)
Conference Proceeding
Cutajar, O., Moradpoor, N., & Jaroucheh, Z. (2021). Using IOTA as an Inter-Vehicular Trust Mechanism in Autonomous Vehicles. In 2021 14th International Conference on Security of Information and Networks (SIN). https://doi.org/10.1109/SIN54109.2021.9699326

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.

Newly Engineered Energy-based Features for Supervised Anomaly Detection in a Physical Model of a Water Supply System   (2021)
Journal Article
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., Russell, G., & Tan, Z. (2021). Newly Engineered Energy-based Features for Supervised Anomaly Detection in a Physical Model of a Water Supply System  . Ad hoc networks, 120, Article 102590. https://doi.org/10.1016/j.adhoc.2021.102590

Industrial Control Systems (ICS) are hardware, network, and software, upon which a facility depends to allow daily operations to function. In most cases society takes the operation of such systems, for example public transport, tap water or electrici... Read More about Newly Engineered Energy-based Features for Supervised Anomaly Detection in a Physical Model of a Water Supply System  .

Implementation and Evaluation of Physical, Hybrid, and Virtual Testbeds for Cybersecurity Analysis of Industrial Control Systems (2021)
Journal Article
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., Russell, G., & Porcel-Bustamante, J. (2021). Implementation and Evaluation of Physical, Hybrid, and Virtual Testbeds for Cybersecurity Analysis of Industrial Control Systems. Symmetry, 13(3), Article 519. https://doi.org/10.3390/sym13030519

Industrial Control Systems are an essential part of our daily lives and can be found in industries such as oil, utilities, and manufacturing. Rapid growth in technology has introduced industrial components with network capabilities that allow them to... Read More about Implementation and Evaluation of Physical, Hybrid, and Virtual Testbeds 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)
Conference Proceeding
Robles-Durazno, A., Moradpoor, N., McWhinnie, J., & Russell, G. (2020). Real-time anomaly intrusion detection for a clean water supply system, utilising machine learning with novel energy-based features. In 2020 International Joint Conference on Neural Networks (IJCNN). https://doi.org/10.1109/IJCNN48605.2020.9207462

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.

Insider Threat Detection Using Supervised Machine Learning Algorithms on an Extremely Imbalanced Dataset (2020)
Journal Article
Moradpoor, N., & Hall, A. (2020). Insider Threat Detection Using Supervised Machine Learning Algorithms on an Extremely Imbalanced Dataset. International Journal of Cyber Warfare and Terrorism, 10(2), https://doi.org/10.4018/IJCWT.2020040101

An insider threat can take on many forms and fall under different categories. This includes: malicious insider, careless/unaware/uneducated/naïve employee, and third-party contractor. A malicious insider, which can be a criminal agent recruited as a... Read More about Insider Threat Detection Using Supervised Machine Learning Algorithms on an Extremely Imbalanced Dataset.

Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks Against Two Objective Functions Using a Novel Dataset (2020)
Journal Article
Foley, J., Moradpoor, N., & Ochen, H. (2020). Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks Against Two Objective Functions Using a Novel Dataset. Security and Communication Networks, 2020, Article 2804291. https://doi.org/10.1155/2020/2804291

One of the important features of Routing Protocol for Low-Power and Lossy Networks (RPL) is Objective Function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the other hand, detecting a combination of attac... Read More about Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks Against Two Objective Functions Using a Novel Dataset.