2026-04-17T17:42:28.104Z http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai

ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai:3:8788 2022-05-23T22:51:55Z 3

Novel Harris Hawks Optimization and Deep Neural Network Approach for Intrusion Detection, Chapter in AIS Algorithms for Intelligent Systems: IJCACI 2021: International Joint Conference on Advances in Computational Intelligence, Springer 2022 http://ezaposleni.singidunum.ac.rs/rest/sciNaucniRezultati/oai/record/3/8788 https://link.springer.com/chapter/10.1007/978-981-19-0332-8_17 M. Zivkovic N. Bacanin J. Arandjelovic A. Rakic I. Strumberger P. Joseph Intrusion detection systems attempt to identify assaults while they occur or after they have occurred and they detect abnormal behavior in a network of computer systems in order to identify whether the activity is hostile or unlawful, allowing a response to the violation. Intrusion detection systems gather network traffic data from a specific location on the network or computer system and utilize it to safeguard hardware and software assets against malicious attacks. These systems employ high-dimensional datasets with a high number of redundant and irrelevant features and a large number of samples. One of the most significant challenges from this domain is the analysis and classification of such a vast amount of heterogeneous data. The utilization of machine learning models is necessary. The method proposed in this paper represents a hybrid approach between recently devised yet well-known, harris hawks optimization metaheuristics and deep neural network machine learning model. Since the basic harris hawks optimization exhibits some deficiencies, its improved version is used for dimensionality reduction, followed by the classification executed by the deep neural network model. Proposed approach is tested against well-known NSL-KDD and KDD Cup 99 Kaggle datasets. Comparative analysis with other similar methods proved the robustness of the presented technique when metrics like accuracy, precision, recall, F1-score are taken into account. bookPart Springer, Singapore 239 250 10.1007/978-981-19-0332-8_17 AIS Algorithms for Intelligent Systems: IJCACI 2021: International Joint Conference on Advances in Computational Intelligence