All Outputs (89)

Assuring privacy of AI-powered community driven Android code vulnerability detection. (2025)
Presentation / Conference Contribution
SENANAYAKE, J., KALUTARAGE, H., PIRAS, L., AL-KADRI, M.O. and PETROVSKI, A. 2025. Assuring privacy of AI-powered community driven Android code vulnerability detection. In Garcia-Alfaro, J., Kalutarage, H., Yanai, N. et al. (eds.) Computer security: ESORICS 2024 international workshops: revised selected papers from the proceedings of eleven international workshops held in conjunction with the 29th European Symposium on Research in Computer Security (ESORICS 2024), 16-20 September 2024, Bydgoszcz, Poland. Part II. Lecture notes in computer science, 15264. Cham: Springer [online], pages 457-476. Available from: https://doi.org/10.1007/978-3-031-82362-6_27

The challenge of training AI models is heightened by the limited availability of data, particularly when public datasets are insufficient. While obtaining data from private sources may seem like a viable solution, privacy concerns often prevent data... Read More about Assuring privacy of AI-powered community driven Android code vulnerability detection..

DataDRILL: resilience testbed for industrial cyber-physical systems. (2025)
Presentation / Conference Contribution
ARIFEEN, M., KOTENKO, I., PETROVSKI, A. and HASSARD, P. 2025. DataDRILL: resilience testbed for industrial cyber-physical systems. In Proceedings of the 17th International conference on communication systems and networks 2025 (COMSNETS 2025), 6-10 January 2025, Bengaluru, India. Piscataway: IEEE [online], pages 1195-1200. Available from: https://doi.org/10.1109/COMSNETS63942.2025.10885712

Testbeds and datasets are essential tools used in experimental work, risk assessment and validation of industrial cyber-physical systems (CPS) with the capability of seamless automation and control. Due to complexity of real CPS and the criticality o... Read More about DataDRILL: resilience testbed for industrial cyber-physical systems..

Exponential degradation model based remaining life prediction for tools of milling machine. (2024)
Presentation / Conference Contribution
ARIFEEN, M., PETROVSKI, A., HASAN, M.J. and AHMAD, Z. 2024. Exponential degradation model based remaining life prediction for tools of milling machine. In Kovalev, S., Kotenko, I., Sukhanov, A., Li, Y. and Li Y. (eds.) Proceedings of the 8th Intelligent information technologies for industry international scientific conference (IITI'24), 1-7 November 2024, Harbin, China. Lecture notes in networks and systems, 1209. Cham: Springer [online], volume 1, pages 355-365. Available from: https://doi.org/10.1007/978-3-031-77688-5_34

Cutting tools of milling machines are prone to failure, and it is essential to predict their remaining useful life to ensure cost-effective maintenance in the manufacturing industry. Recent studies have shown that deep learning techniques can effecti... Read More about Exponential degradation model based remaining life prediction for tools of milling machine..

Insider threat detection within operational technology using digital twins. (2024)
Presentation / Conference Contribution
PETROVSKI, A., KOTENKO, I., ARIFEEN, M., ABRAMENKO, G. and SOBOLEV, P. 2024. Insider threat detection within operational technology using digital twins. In Kovalev, S., Kotenko, I., Sukhanov, A., Li, Y. and Li Y. (eds.) Proceedings of the 8th Intelligent information technologies for industry international scientific conference (IITI'24), 1-7 November 2024, Harbin, China. Lecture notes in networks and systems, 1210. Cham: Springer [online], volume 2, pages 25-34. Available from: https://doi.org/10.1007/978-3-031-77411-9_3

Managing unintentional insider threat is a growing challenge in digital industries because the biggest threat to operational technologies (OT) originates internally, irrespective of the type or size of the organisation. Data breaches and other advanc... Read More about Insider threat detection within operational technology using digital twins..

Graph-variational convolutional autoencoder-based fault detection and diagnosis for photovoltaic arrays. (2024)
Journal Article
ARIFEEN, M., PETROVSKI, A., HASAN, M.J., NOMAN, K., NAVID, W.U. and HARUNA, A. 2024. Graph-variational convolutional autoencoder-based fault detection and diagnosis for photovoltaic arrays. Machines [online], 12(12), article 894. Available from: https://doi.org/10.3390/machines12120894

Solar energy is a critical renewable energy source, with solar arrays or photovoltaic systems widely used to convert solar energy into electrical energy. However, solar array systems can develop faults and may exhibit poor performance. Diagnosing and... Read More about Graph-variational convolutional autoencoder-based fault detection and diagnosis for photovoltaic arrays..

Enhancing Android application security through source code vulnerability mitigation using artificial intelligence: a privacy-preserved, community-driven, federated-learning-based approach. (2024)
Thesis
SENANAYAKE, J.M.D. 2024. Enhancing Android application security through source code vulnerability mitigation using artificial intelligence: a privacy-preserved, community-driven, federated-learning-based approach. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2801183

As technology advances, Android devices and apps are rapidly increasing. It is crucial to adhere to security protocols during app development, especially as many apps lack sufficient safeguards. Despite the use of automated tools for risk mitigation,... Read More about Enhancing Android application security through source code vulnerability mitigation using artificial intelligence: a privacy-preserved, community-driven, federated-learning-based approach..

Applications of artificial intelligence in geothermal resource exploration: a review. (2024)
Journal Article
ALGAIAR, M., HOSSAIN, M., PETROVSKI, A., LASHIN, A. and FAISAL, N. 2024. Applications of artificial intelligence in geothermal resource exploration: a review. Deep underground science and engineering [online], 3(3): geothermal energy, pages 269-285. Available from: https://doi.org/10.1002/dug2.12122

Artificial intelligence (AI) has become increasingly important in geothermal exploration, significantly improving the efficiency of resource identification. This review examines current AI applications, focusing on the algorithms used, the challenges... Read More about Applications of artificial intelligence in geothermal resource exploration: a review..

Protecting vehicles from cyberattacks: context aware AI-based intrusion detection for vehicle CAN bus security. (2024)
Thesis
RAJAPAKSHA, S. 2024. Protecting vehicles from cyberattacks: context aware AI-based intrusion detection for vehicle CAN bus security. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2801124

Modern automobiles are equipped with a large number of electronic control units (ECUs), which are interconnected through the controller area network (CAN) bus for real-time data exchange. However, the CAN bus lacks security measures, rendering it sus... Read More about Protecting vehicles from cyberattacks: context aware AI-based intrusion detection for vehicle CAN bus security..

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 - 2 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..

Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures. (2024)
Thesis
OTOKWALA, U.J. 2024. Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2571244

Critical Infrastructures (CI) are essential for various aspects of human activities, spanning across different sectors. However, the integration of Internet of Things (IoT) devices into CI has introduced a new dimension to security challenges due to... Read More about Lightweight intrusion detection of attacks on the Internet of Things (IoT) in critical infrastructures..

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..

Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things. (2024)
Journal Article
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2024 Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in Internet of things. International journal of information security [online], 23(4), pages 2559-2581. Available from: https://doi.org/10.1007/s10207-024-00855-7

Embedded systems, including the Internet of things (IoT), play a crucial role in the functioning of critical infrastructure. However, these devices face significant challenges such as memory footprint, technical challenges, privacy concerns, performa... Read More about Optimized common features selection and deep-autoencoder (OCFSDA) for lightweight intrusion detection in 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..

Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. (2024)
Journal Article
SENANAYAKE, J., KALUTARAGE, H., PETROVSKI, A., PIRAS, L. and AL-KADRI, M.O. 2024. Defendroid: real-time Android code vulnerability detection via blockchain federated neural network with XAI. Journal of information security and applications [online], 82, article number 103741. Available from: https://doi.org/10.1016/j.jisa.2024.103741

Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to redu... Read More about Defendroid: real-time Android code vulnerability detection via blockchain 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 - 1 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..

Securing information systems against advanced persistent threats (APTs). (2024)
Thesis
EKE, E.N. 2024. Securing information systems against advanced persistent threats (APTs). Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2445760

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APTs are sophisticated attacks that masquerade their actions to navigate around defenses, breach netw... Read More about Securing information systems against advanced persistent threats (APTs)..

Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D. (2023)
Presentation / Conference Contribution
BANDA, T.M., ZĂVOIANU, A.-C., PETROVSKI, A., WÖCKINGER, D. and BRAMERDORFER, G. 2024. Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D. In Stratulat, S., Marin, M., Negru, V. and Zaharie, D. (eds.) Proceedings of the 25th International symposium on symbolic and numeric algorithms for scientific computing (SYNASC 2023), 11-14 September 2023, Nancy, France. Los Alamitos: IEEE Computer Society [online], pages 186-193. Available from: https://doi.org/10.1109/SYNASC61333.2023.00032

For engineers to create durable and effective electrical assemblies, modelling and controlling heat transfer in rotating electrical machines (such as motors) is crucial. In this paper, we compare the performance of three multi-objective evolutionary... Read More about Optimising linear regression for modelling the dynamic thermal behaviour of electrical machines using NSGA-II, NSGA-III and MOEA/D..