All Outputs (7)

Securing cyber-physical systems with two-level anomaly detection strategy. (2024)
Presentation / Conference Contribution
AHMAD, Z. and PETROVSKI, A. 2024. Securing cyber-physical systems with two-level anomaly detection strategy. In Proceedings of the 7th IEEE (Institute of Electrical and Electronics Engineers) Industrial cyber-physical systems international conference 2024 (ICPS 2024), 12-15 May 2024, St. Louis, USA. Piscataway: IEEE [online], article number 10639983. Available from: https://doi.org/10.1109/ICPS59941.2024.10639983

Cyber-physical system (CPS) represents the integration of digital technologies with physical processes to revolutionize Industry 4.0 by optimizing the industrial processes. However, due to the integration of interconnected devices, the internet, and... Read More about Securing cyber-physical systems with two-level anomaly detection strategy..

Temporal graph convolutional autoencoder based fault detection for renewable energy applications. (2024)
Presentation / Conference Contribution
ARIFEEN, M. and PETROVSKI, A. 2024. Temporal graph convolutional autoencoder based fault detection for renewable energy applications. In Proceedings of the 7th IEEE (Institute of Electrical and Electronics Engineers) Industrial cyber-physical systems international conference 2024 (ICPS 2024), 12-15 May 2024, St. Louis, USA. Piscataway: IEEE [online], article number 10639998. Available from: https://doi.org/10.1109/ICPS59941.2024.10639998

Detecting faults in energy generation systems is a challenging task due to the complex nature of the system, measurement noise, and outliers. Recently, researchers have shown an increasing interest in using data-driven models that utilize sensor data... Read More about Temporal graph convolutional autoencoder based fault detection for renewable energy applications..

Assessing the performance of ethereum and hyperledger fabric under DDoS attacks for cyber-physical systems. (2024)
Presentation / Conference Contribution
JAYADEV, V., MORADPOOR, N. and PETROVSKI, A. 2024. Assessing the performance of ethereum and hyperledger fabric under DDoS attacks for cyber-physical systems. In ARES '24: proceedings of the 19th International conference on Availability, reliability and security, 30 July - 02 August 2024, Vienna, Austria. New York: ACM [online], article number 48. Available from: https://doi.org/10.1145/3664476.3670927

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..

HEADS: hybrid ensemble anomaly detection system for Internet-of-Things networks. (2024)
Presentation / Conference Contribution
AHMAD, Z., PETROVSKI, A., ARIFEEN, M., KHAN, A.S. and SHAH, S.A. 2024. HEADS: hybrid ensemble anomaly detection system for Internet-of-Things networks. In Iliadis, L., Maglogiannis, I., Papaleonidas, A., Pimenidis, E. and Jayne, C. (eds.) Engineering applications on neural networks: proceedings of the 25th International Engineering applications on neural networks 2024 (EANN 2024), 27-30 June 2024, Corfu, Greece. Communications in computer and information science, 2141. Cham: Springer [online], pages 178-190. Available from: https://doi.org/10.1007/978-3-031-62495-7_14

The rapid expansion of Internet-of-Things (IoT) devices has revolutionized connectivity, facilitating the exchange of extensive data within IoT networks via the traditional internet. However, this innovation has also increased security concerns due t... Read More about HEADS: hybrid ensemble anomaly detection system for Internet-of-Things networks..

A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things. (2024)
Presentation / Conference Contribution
OTOKWALA, U., ARIFEEN, M. and PETROVSKI, A. 2024. A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things. In Panoutsos, G., Mihaylova, L.S. and Mahfouf, M. (eds.) Advances in computational intelligence systems: contributions presented at the 21st UK workshop on computational intelligence (UKCCI 2022), 7-9 September 2022, Sheffield, UK. Advances in intelligent systems and computing, 1454. Cham: Springer [online], pages 238-249. Available from: https://doi.org/10.1007/978-3-031-55568-8_20

The detection of zero-day attacks in the IoT network is a challenging task due to unknown security vulnerabilities. Also, the unavailability of the data makes it difficult to train a machine learning (ML) model about new vulnerabilities. The existing... Read More about A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things..

FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI. (2024)
Presentation / Conference Contribution
SENANAYAKE, J., KALUTARAGE, H., PETROVSKI, A., AL-KADRI, M.O. and PIRAS, L. 2024. FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI. In Katsikas, S. et al. (eds.) Computer security: revised selected papers from the proceedings of the International workshops of the 28th European symposium on research in computer security (ESORICS 2023 International Workshops), 25-29 September 2023, The Hague, Netherlands. Lecture notes in computer science, 14399. Cham: Springer [online], part II, pages 426-441. Available from: https://doi.org/10.1007/978-3-031-54129-2_25

Adhering to security best practices during the development of Android applications is of paramount importance due to the high prevalence of apps released without proper security measures. While automated tools can be employed to address vulnerabiliti... Read More about FedREVAN: real-time detection of vulnerable android source code through federated neural network with XAI..

CAN-MIRGU: a comprehensive CAN bus attack dataset from moving vehicles for intrusion detection system evaluation. (2024)
Presentation / Conference Contribution
RAJAPAKSHA, S., MADZUDZO, G., KALUTARAGE, H., PETROVSKI, A. and AL-KADRI, M.O. 2024. CAN-MIRGU: a comprehensive CAN bus attack dataset from moving vehicles for intrusion detection system evaluation. In Proceedings of the 2nd Vehicle security and privacy symposium 2024 (VehicleSec 2024), co-located with the 2024 Network and distributed system security symposium (NDSS 2024), 26 February - 01 March 2024, San Diego, CA, USA. San Diego, CA: NDSS [online], paper 43. Available from: https://doi.org/10.14722/vehiclesec.2024.23043

The Controller Area Network (CAN Bus) has emerged as the de facto standard for in-vehicle communication. However, the CAN bus lacks security features, such as encryption and authentication, making it vulnerable to cyberattacks. In response, the curre... Read More about CAN-MIRGU: a comprehensive CAN bus attack dataset from moving vehicles for intrusion detection system evaluation..