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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..

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..