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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..

Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus. (2019)
Conference Proceeding
KALUTARAGE, H.K., AL-KADRI, M.O., CHEAH, M. and MADZUDZO, G. 2019. Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus. In Hof, H.-J., Fritz, M., Kraub, C. and Wasenmüller, O. (eds.). Proceedings of 2019 Computer science in cars symposium (CSCS 2019), 8 October 2019, Kaiserslautern, Germany. New York: ACM [online], article number 7. Available from: https://doi.org/10.1145/3359999.3360496

Automotive electronics is rapidly expanding. An average vehicle contains million lines of software codes, running on 100 of electronic control units (ECUs), in supporting number of safety, driver assistance and infotainment functions. These ECUs are... Read More about Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus..

Anomaly detection in network traffic using dynamic graph mining with a sparse autoencoder. (2019)
Conference Proceeding
JIA, G., MILLER, P., HONG, X., KALUTARAGE, H. and BAN, T. 2019. Anomaly detection in network traffic using dynamic graph mining with a sparse autoencoder. In Proceedings of 18th Institution of Electrical and Electronics Engineers (IEEE) International trust, security and privacy in computing and communications conference, co-located with 13th Institution of Electrical and Electronics Engineers (IEEE) International big data science and engineering conference (TrustCom/BigDataSE), 5-8 August 2019, Rotorua, New Zealand. Piscataway: IEEE [online], pages 458-465. Available from: https://doi.org/10.1109...om/BigDataSE.2019.00068

Network based attacks on ecommerce websites can have serious economic consequences. Hence, anomaly detection in dynamic network traffic has become an increasingly important research topic in recent years. This paper proposes a novel dynamic Graph and... Read More about Anomaly detection in network traffic using dynamic graph mining with a sparse autoencoder..

Towards a threat assessment framework for apps collusion. (2017)
Journal Article
KALUTARAGE, H.K., NGUYEN, H.N. and SHAIKH, S.A. 2017. Towards a threat assessment framework for apps collusion. Telecommunication systems [online], 66(3), pages 417-430. Available from: https://doi.org/10.1007/s11235-017-0296-1

App collusion refers to two or more apps working together to achieve a malicious goal that they otherwise would not be able to achieve individually. The permissions based security model of Android does not address this threat as it is rather limited... Read More about Towards a threat assessment framework for apps collusion..

Detecting stealthy attacks: efficient monitoring of suspicious activities on computer networks. (2015)
Journal Article
KALUTARAGE, H.K., SHAIKH, S.A., WICKRAMASINGHE, I.P., ZHOU, Q. and JAMES, A.E. 2015. Detecting stealthy attacks: efficient monitoring of suspicious activities on computer networks. Computers and electrical engineering [online], 47, pages 327-344. Available from: https://doi.org/10.1016/j.compeleceng.2015.07.007

Stealthy attackers move patiently through computer networks – taking days, weeks or months to accomplish their objectives in order to avoid detection. As networks scale up in size and speed, monitoring for such attack attempts is increasingly a chall... Read More about Detecting stealthy attacks: efficient monitoring of suspicious activities on computer networks..


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