Lightweight Intrusion Detection for Edge Computing Networks using Deep Forest and Bio-Inspired Algorithms

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Publikace nespadá pod Ekonomicko-správní fakultu, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
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BANGUI Hind BÜHNOVÁ Barbora

Rok publikování 2022
Druh Článek v odborném periodiku
Časopis / Zdroj Computers and Electrical Engineering
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www https://doi.org/10.1016/j.compeleceng.2022.107901
Doi http://dx.doi.org/10.1016/j.compeleceng.2022.107901
Klíčová slova Mobile edge computing; Deep learning; Bio-inspired computing
Popis Today, incorporating advanced machine learning techniques into intrusion detection systems (IDSs) plays a crucial role in securing mobile edge computing systems. However, the mobility demands of our modern society require more advanced IDSs to make a good trade-off between coping with the rapid growth of traffic data and responding to attacks. Thus, in this paper, we propose a lightweight distributed IDS that exploits the advantages of centralized platforms to train and learn from large amounts of data. We investigate the benefits of two promising bio-inspired optimization algorithms, namely Ant Lion Optimization and Ant Colony Optimization, to find the optimal data representation for the classification process. We use Deep Forest as a classifier to detect intrusive actions more robustly and generate as few false positives as possible. The experiment results show that the proposed approach can enhance the reliability of lightweight intrusion detection systems in terms of accuracy and execution time.
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