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All Outputs (50)

Lightweight Blockchain Prototype for Food Supply Chain Management (2024)
Presentation / Conference Contribution
Rusakov, A., Moradpoor, N., & Akbarzadeh, A. (2024, December). Lightweight Blockchain Prototype for Food Supply Chain Management. Presented at 17th International Conference on Security of Information and Networks, Online

The modern food supply chain often involves multiple layers of participants spread across different countries and continents. This complex system offers significant benefits to businesses worldwide; however, it also presents several challenges. One m... Read More about Lightweight Blockchain Prototype for Food Supply Chain Management.

Enhancing AI-Generated Image Detection with a Novel Approach and Comparative Analysis (2024)
Presentation / Conference Contribution
Weir, S., Khan, M. S., Moradpoor, N., & Ahmad, J. (2024, December). Enhancing AI-Generated Image Detection with a Novel Approach and Comparative Analysis. Paper presented at SINCONF 2024: 17th International Conference on Security of Information and Networks (SIN'24), Online

This study explores advancements in AI-generated image detection, emphasizing the increasing realism of images, including deepfakes, and the need for effective detection methods. Traditional Convolutional Neural Networks (CNNs) have shown success but... Read More about Enhancing AI-Generated Image Detection with a Novel Approach and Comparative Analysis.

Enhancing Security and Privacy in Federated Learning for Connected Autonomous Vehicles with Lightweight Blockchain and Binius Zero- Knowledge Proofs (2024)
Presentation / Conference Contribution
Andriambelo, N. H., & Moradpoor, N. (2024, December). Enhancing Security and Privacy in Federated Learning for Connected Autonomous Vehicles with Lightweight Blockchain and Binius Zero- Knowledge Proofs. Paper presented at SINCONF 2024: 17th International Conference on Security of Information and Networks (SIN'24), Online

The rise of autonomous vehicles (AVs) brings with it the need for secure and privacy-preserving machine learning models. Federated learning (FL) allows AVs to collaboratively train models while keeping raw data localized. However, traditional FL syst... Read More about Enhancing Security and Privacy in Federated Learning for Connected Autonomous Vehicles with Lightweight Blockchain and Binius Zero- Knowledge Proofs.

EV-IRP Manager: An Electric Vehicle Incident Response Playbook Manager and Visualizer Toolkit (2024)
Presentation / Conference Contribution
Alpdag, K., Moradpoor, N., Hasina Andriambelo, N., Wooderson, P., & Maglaras, L. (2024, December). EV-IRP Manager: An Electric Vehicle Incident Response Playbook Manager and Visualizer Toolkit. Presented at SINCONF 2024: 17th International Conference on Security of Information and Networks, Online

In the rapidly evolving realm of electric vehicle technology, safeguarding the cybersecurity of both electric vehicles and their charging infrastructure has become fundamental. The integration of electric vehicles and their charging stations into the... Read More about EV-IRP Manager: An Electric Vehicle Incident Response Playbook Manager and Visualizer Toolkit.

Machine Learning for Smart Healthcare Management Using IoT (2024)
Book Chapter
Yigit, Y., Duran, K., Moradpoor, N., Maglaras, L., Van Huynh, N., & Canberk, B. (2024). Machine Learning for Smart Healthcare Management Using IoT. In IoT and ML for Information Management: A Smart Healthcare Perspective (135-166). Springer. https://doi.org/10.1007/978-981-97-5624-7_4

This chapter explores the significant impact of Machine Learning (ML) and the Internet of Things (IoT) on smart healthcare management, marking a new era of innovation with enhanced patient care and health outcomes. The fusion of IoT devices for real-... Read More about Machine Learning for Smart Healthcare Management Using IoT.

Enhancing Cloud Computing Security Through Blockchain-Based Communication for Electronic Health Records (2024)
Presentation / Conference Contribution
Noyon, M. S. I., Moradpoor, N., Maglaras, L., & Ahmad, J. (2024, April). Enhancing Cloud Computing Security Through Blockchain-Based Communication for Electronic Health Records. Presented at DCOSS-IoT 2024, Abu Dhabi, United Arab Emirates

The health sector stands as one of the most crucial and vulnerable domains, harbouring extensive personal data. Particularly, Electronic Health Records store information in electronic media where users lack control over their data. Unauthorized acces... Read More about Enhancing Cloud Computing Security Through Blockchain-Based Communication for Electronic Health Records.

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.

Towards a Cyberbullying Detection Approach: Fine-Tuned Contrastive Self- Supervised Learning for Data Augmentation (2024)
Journal Article
Alharigy, L., Alnuaim, H., Moradpoor, N., & Tan, T. (online). Towards a Cyberbullying Detection Approach: Fine-Tuned Contrastive Self- Supervised Learning for Data Augmentation. International Journal of Data Science and Analytics, https://doi.org/10.1007/s41060-024-00607-9

Cyberbullying on social media platforms is pervasive and challenging to detect due to linguistic subtleties and the need for extensive data annotation. We introduce a Deep Contrastive Self-Supervised Learning (DCSSL) model that integrates a Natural L... Read More about Towards a Cyberbullying Detection Approach: Fine-Tuned Contrastive Self- Supervised Learning for Data Augmentation.

APOLLO: A Proximity-Oriented, Low-Layer Orchestration Algorithm for Resources Optimization in Mist Computing (2024)
Journal Article
Babaghayou, M., Chaib, N., Maglaras, L., Yigit, Y., Amine Ferrag, M., Marsh, C., & Moradpoor, N. (online). APOLLO: A Proximity-Oriented, Low-Layer Orchestration Algorithm for Resources Optimization in Mist Computing. Wireless Networks, https://doi.org/10.1007/s11276-024-03791-5

The fusion of satellite technologies with the Internet of Things (IoT) has propelled the evolution of mobile computing, ushering in novel communication paradigms and data management strategies. Within this landscape, the efficient management of compu... Read More about APOLLO: A Proximity-Oriented, Low-Layer Orchestration Algorithm for Resources Optimization in Mist Computing.

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.

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.

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 Hybrid Deep Learning-based Intrusion Detection System for IoT Networks (2023)
Journal Article
Khan, N. W., Alshehri, M. S., Khan, M. A., Almakdi, S., Moradpoor, N., Alazeb, A., Ullah, S., Naz, N., & Ahmad, J. (2023). A Hybrid Deep Learning-based Intrusion Detection System for IoT Networks. Mathematical Biosciences and Engineering, 20(8), 13491-13520. https://doi.org/10.3934/mbe.2023602

The Internet of Things (IoT) is a rapidly evolving technology with a wide range of potential applications, but the security of IoT networks remains a major concern. The existing system needs improvement in detecting intrusions in IoT networks. Severa... Read More about A Hybrid Deep Learning-based Intrusion Detection System for IoT Networks.

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.

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)
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.

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.