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Advanced persistent threats detection based on deep learning approach. (2023)
Conference Proceeding
EKE, H.N. and PETROVSKI, A. [2023]. Advanced persistent threats detection based on deep learning approach. To be presented at the 6th IEEE (Institute of Electrical and Electronics Engineers) Industrial cyber physical systems international conference 2023 (ICPS 2023), 8-11 May 2023, Wuhan, China.

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. APT is a sophisticated attack that masquerade their actions to navigates around defenses, breach netw... Read More about Advanced persistent threats detection based on deep learning approach..

Topology for preserving feature correlation in tabular synthetic data. (2022)
Conference Proceeding
ARIFEEN, M. and PETROVSKI, A. 2022. Topology for preserving feature correlation in tabular synthetic data. In Proceedings of the 15th IEEE (Institute of Electrical and Electronics Engineers) International conference on security of information and networks 2022 (SINCONF 2022), 11-13 November 2022, Sousse, Tunisia. Piscataway: IEEE [online], pages 61-66. Available from: https://doi.org/10.1109/SIN56466.2022.9970505

Tabular synthetic data generating models based on Generative Adversarial Network (GAN) show significant contributions to enhancing the performance of deep learning models by providing a sufficient amount of training data. However, the existing GAN-ba... Read More about Topology for preserving feature correlation in tabular synthetic data..

Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security. (2022)
Conference Proceeding
RAJAPAKSHA, S., KALUTARAGE, H., AL-KADRI, M.O., MADZUDZO, G. and PETROVSKI, A.V. 2022. Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security. In Jančárková, T., Visky, G. and Winther, I. (eds.). Proceedings of 14th International conference on Cyber conflict 2022 (CyCon 2022): keep moving, 31 May - 3 June 2022, Tallinn, Estonia. Tallinn: CCDCOE, pages 309-330. Hosted on IEEE Xplore [online]. Available from: https://doi.org/10.23919/CyCon55549.2022.9811048

The growth of information technologies has driven the development of the transportation sector, including connected and autonomous vehicles. Due to its communication capabilities, the controller area network (CAN) is the most widely used in-vehicle c... Read More about Keep the moving vehicle secure: context-aware intrusion detection system for in-vehicle CAN bus security..

Developing secured android applications by mitigating code vulnerabilities with machine learning. (2022)
Conference Proceeding
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PETROVSKI, A. and PIRAS, L. 2022. Developing secured android applications by mitigating code vulnerabilities with machine learning. In ASIA CCS '22: proceedings of the 17th ACM (Association for Computing Machinery) Asia conference on computer and communications security 2022 (ASIA CCS 2022), 30 May - 3 June 2022, Nagasaki, Japan. New York: ACM [online], pages 1255-1257. Available from: https://doi.org/10.1145/3488932.3527290

Mobile application developers sometimes might not be serious about source code security and publish apps to the marketplaces. Therefore, it is essential to have a fully automated security solutions generator to integrate security-by-design into the d... Read More about Developing secured android applications by mitigating code vulnerabilities with machine learning..

Comparative study of malware detection techniques for industrial control systems. (2021)
Conference Proceeding
REID, D., HARRIS, I. and PETROVSKI, A. 2021. Comparative study of malware detection techniques for industrial control systems. In Moradpoor, N., Elçi, A. and Petrovski, A. (eds.) Proceedings of 14th International conference on Security of information and networks 2021 (SIN 2021), 15-17 December 2021, [virtual conference]. Piscataway: IEEE [online], article 19. Available from: https://doi.org/10.1109/SIN54109.2021.9699167

Industrial Control Systems are essential to managing national critical infrastructure, yet the security of these systems historically relies on isolation. The adoption of modern software solutions, and the unique challenges presented by legacy system... Read More about Comparative study of malware detection techniques for industrial control systems..

Improving intrusion detection through training data augmentation. (2021)
Conference Proceeding
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2021. Improving intrusion detection through training data augmentation. In Moradpoor, N., Elçi, A. and Petrovski, A. (eds.) Proceedings of 14th International conference on Security of information and networks 2021 (SIN 2021), 15-17 December 2021, [virtual conference]. Piscataway: IEEE [online], article 17. Available from: https://doi.org/10.1109/SIN54109.2021.9699293

Imbalanced classes in datasets are common problems often found in security data. Therefore, several strategies like class resampling and cost-sensitive training have been proposed to address it. In this paper, we propose a data augmentation strategy... Read More about Improving intrusion detection through training data augmentation..

Automated microsegmentation for lateral movement prevention in industrial Internet of Things (IIoT). (2021)
Conference Proceeding
ARIFEEN, M., PETROVSKI, A. and PETROVSKI, S. 2021. Automated microsegmentation for lateral movement prevention in industrial Internet of Things (IIot). In Moradpoor, N., Elçi, A. and Petrovski, A. (eds.) Proceedings of 14th International conference on Security of information and networks 2021 (SIN 2021), 15-17 December 2021, [virtual conference]. Piscataway: IEEE [online], article 28. Available from: https://doi.org/10.1109/SIN54109.2021.9699232

The integration of the IoT network with the Operational Technology (OT) network is increasing rapidly. However, this incorporation of IoT devices into the OT network makes the industrial control system vulnerable to various cyber threats. Hacking an... Read More about Automated microsegmentation for lateral movement prevention in industrial Internet of Things (IIoT)..

Effective detection of cyber attack in a cyber-physical power grid system. (2021)
Conference Proceeding
OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2021. Effective detection of cyber attack in a cyber-physical power grid system. In Arai, K. (ed) Advances in information and communication: proceedings of Future of information and communication conference (FICC 2021), 29-30 April 2021, Vancouver, Canada. Advances in intelligent systems and computing, 1363. Cham: Springer [online], 1, pages 812-829. Available from: https://doi.org/10.1007/978-3-030-73100-7_57

Advancement in technology and the adoption of smart devices in the operation of power grid systems have made it imperative to ensure adequate protection for the cyber-physical power grid system against cyber-attacks. This is because, contemporary cyb... Read More about Effective detection of cyber attack in a cyber-physical power grid system..

Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data. (2020)
Conference Proceeding
MAJDANI, F., BATIK, L., PETROVSKI, A. and PETROVSKI, S. 2020. Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data. In Proceedings of the 13th Security of information and networks international conference 2020 (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], article number 4, pages 1-8. Available from: https://doi.org/10.1145/3433174.3433613

This paper looks at potential vulnerabilities of the Smart Grid energy infrastructure to data injection cyber-attacks and the means of addressing these vulnerabilities through intelligent data analysis. Efforts are being made by multiple groups to pr... Read More about Detecting malicious signal manipulation in smart grids using intelligent analysis of contextual data..

Detection of false command and response injection attacks for cyber physical systems security and resilience. (2020)
Conference Proceeding
EKE, H., PETROVSKI, A. and AHRIZ, H. 2020. Detection of false command and response injection attacks for cyber physical systems security and resilience. In Proceedings of the 13th Security of information and networks international conference 2020 (SIN 2020), 4-7 November 2020, Merkez, Turkey. New York: ACM [online], article number 10, pages 1-8. Available from: https://doi.org/10.1145/3433174.3433615

The operational cyber-physical system (CPS) state, safety and resource availability is impacted by the safety and security measures in place. This paper focused on i) command injection (CI) attack that alters the system behaviour through injection of... Read More about Detection of false command and response injection attacks for cyber physical systems security and resilience..

Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm. (2019)
Conference Proceeding
ZAKARIYYA, I., AL-KADRI, M.O., KALUTARGE, H. and PETROVSKI, A. 2019. Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm. In Obaidat, M. and Samarati, P. (eds.) Proceedings of the 16th International security and cryptography conference (SECRYPT 2019), co-located with the 16th International joint conference on e-business and telecommunications (ICETE 2019), 26-28 July 2019, Prague, Czech Republic. Setúbal, Portugal: SciTePress [online], 2, pages 523-528. Available from: https://doi.org/10.5220/0008119205230528.

Using Machine Learning (ML) for Internet of Things (IoT) security monitoring is a challenge. This is due to their resource constraint nature that limits the deployment of resource-hungry monitoring algorithms. Therefore, the aim of this paper is to i... Read More about Reducing computational cost in IoT cyber security: case study of artificial immune system algorithm..

The use of machine learning algorithms for detecting advanced persistent threats. (2019)
Conference Proceeding
EKE, H.N., PETROVSKI, A. and AHRIZ, H. 2019. The use of machine learning algorithms for detecting advanced persistent threats. In Makarevich, O., Babenko, L., Anikeev, M., Elci, A. and Shahriar, H. (eds.). Proceedings of the 12th Security of information and networks international conference 2019 (SIN 2019), 12-15 September 2019, Sochi, Russia. New York: ACM [online], article No. 5. Available from: https://doi.org/10.1145/3357613.3357618

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. Due to their capability to navigates around defenses and to evade detection for a prolonged period of... Read More about The use of machine learning algorithms for detecting advanced persistent threats..

Overlap-based undersampling for improving imbalanced data classification. (2018)
Conference Proceeding
VUTTIPITTAYAMONGKOL, P., ELYAN, E., PETROVSKI, A. and JAYNE, C. 2018. Overlap-based undersampling for improving imbalanced data classification. In Yin, H., Camacho, D., Novais, P. and Tallón-Ballesteros, A. (eds.) Intelligent data engineering and automated learning: proceedings of the 19th International intelligent data engineering and automated learning conference (IDEAL 2018), 21-23 November 2018, Madrid, Spain. Lecture notes in computer science, 11341. Cham: Springer [online], pages 689-697. Available from: https://doi.org/10.1007/978-3-030-03493-1_72

Classification of imbalanced data remains an important field in machine learning. Several methods have been proposed to address the class imbalance problem including data resampling, adaptive learning and cost adjusting algorithms. Data resampling me... Read More about Overlap-based undersampling for improving imbalanced data classification..

Generic application of deep learning framework for real-time engineering data analysis. (2018)
Conference Proceeding
MAJDANI, F., PETROVSKI, A. and PETROVSKI, S. 2018. Generic application of deep learning framework for real-time engineering data analysis. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489356. Available from: https://doi.org/10.1109/IJCNN.2018.8489356

The need for computer-assisted real-time anomaly detection in engineering data used for condition monitoring is apparent in various applications, including the oil and gas, automotive industries and many other engineering domains. To reduce the relia... Read More about Generic application of deep learning framework for real-time engineering data analysis..

Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry. (2018)
Conference Proceeding
VILELA, M., OLUYEMI, G. and PETROVSKI, A. 2018. Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry. In Proceedings of the 2018 IEEE international conference on fuzzy systems (FUZZ-IEEE 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article ID 8491628. Available from: https://doi.org/10.1109/FUZZ-IEEE.2018.8491628

To manage uncertainty in reservoir development projects, the Value of Information is one of the main factors on which the decision is based to determine whether it is necessary to acquire additional data. However, subsurface data is not always precis... Read More about Fuzzy data analysis methodology for the assessment of value of information in the oil and gas industry..

Botnet detection in the Internet of Things using deep learning approaches. (2018)
Conference Proceeding
MCDERMOTT, C.D., MAJDANI, F. and PETROVSKI, A.V. 2018. Botnet detection in the Internet of Things using deep learning approaches. In Proceedings of the 2018 International joint conference on neural networks (IJCNN 2018), 8-13 July 2018, Rio de Janeiro, Brazil. Piscataway, NJ: IEEE [online], article number 8489489. Available from: https://doi.org/10.1109/IJCNN.2018.8489489

The recent growth of the Internet of Things (IoT) has resulted in a rise in IoT based DDoS attacks. This paper presents a solution to the detection of botnet activity within consumer IoT devices and networks. A novel application of Deep Learning is u... Read More about Botnet detection in the Internet of Things using deep learning approaches..

Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications. (2018)
Conference Proceeding
OCHEI, L.C., PETROVSKI, A. and BASS, J.M. 2018. Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications. In Proceedings of the 2018 IEEE international symposium on innovations in intelligent systems and applications (INISTA 2018), 3-5 July 2018, Thessaloniki, Greece. New York: IEEE [online], article ID 8466315. Available from: https://doi.org/10.1109/INISTA.2018.8466315

A multitenant cloud-application that is designed to use several components needs to implement the required degree of isolation between the components when the workload changes. The highest degree of isolation results in high resource consumption and... Read More about Evolutionary computation for optimal component deployment with multitenancy isolation in cloud-hosted applications..

Towards situational awareness of botnet activity in the Internet of Things (2018)
Conference Proceeding
MCDERMOTT, C.D., PETROVSKI, A.V. and MAJDANI, F. 2018. Towards situational awareness of botnet activity in the Internet of Things. In Proceedings of the 2018 International conference on cyber situational awareness, data analytics and assessment (Cyber SA 2018): cyber situation awareness as a tool for analysis and insight, 11-12 June 2018, Glasgow, UK. Piscataway: IEEE [online], article number 8551408. Available from: https://doi.org/10.1109/CyberSA.2018.8551408

An IoT botnet detection model is designed to detect anomalous attack traffic utilised by the mirai botnet malware. The model uses a novel application of Deep Bidirectional Long Short Term Memory based Recurrent Neural Network (BLSTMRNN), in conjuncti... Read More about Towards situational awareness of botnet activity in the Internet of Things.

Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance. (2016)
Conference Proceeding
PETROVSKI, A., RATTADILOK, P. and PETROVSKII, S. 2016. Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 161-175. Available from: https://doi.org/10.1007/978-3-319-44188-7_12 161-175. Available from: https://doi.org/10.1007/978-3-319-44188-7_12

An adaptive framework for building intelligent measurement systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making intelligent decisions related to the presence of anomalies in... Read More about Intelligent measurement in unmanned aerial cyber physical systems for traffic surveillance..

Designing a context-aware cyber physical system for smart conditional monitoring of platform equipment. (2016)
Conference Proceeding
MAJDANI, F., PETROVSKI, A. and DOOLAN, D. 2016. Designing a context-aware cyber physical system for smart conditional monitoring of platform equipment. In Jayne, C. and Iliadis, L. (eds.) Engineering applications of neural networks: proceedings of the 17th International engineering applications of neural networks conference (EANN 2016), 2-5 September 2016, Aberdeen, UK. Communications in computer and information science, 629. Cham: Springer [online], pages 198-210. Available from: https://doi.org/10.1007/978-3-319-44188-7_15

An adaptive multi-tiered framework, which can be utilised for designing a context-aware cyber physical system is proposed and applied within the context of assuring offshore asset integrity. Adaptability is achieved through the combined use of machin... Read More about Designing a context-aware cyber physical system for smart conditional monitoring of platform equipment..