Anomaly-based Intrusion Detection in IoT using Enhanced Kepler Optimization Algorithm for Feature Selection
Abstract
The proliferation of Internet of Things (IoT) devices has increased the risk of botnet attacks due to the inherent vulnerabilities of IoT networks. To mitigate this threat, this study presents an anomaly-based intrusion detection framework that incorporates the Enhanced Kepler Optimization Algorithm (EKOA) for feature selection. EKOA integrates adaptive processes, such as dynamic adaptation, oscillatory chaotic force, crosswise solution formation, and optimization based on elites, in an effort to balance exploitation and exploration in favor of enhancing convergence speed alongside solution diversity. The selected features are evaluated using K-Nearest Neighbor (KNN) and Decision Tree (DT) classifiers. Experiments were conducted on typical IoT datasets, i.e., Mirai and Gafgyt. Accuracy, AUC, G-mean, and precision were also used for performance evaluation. The new system achieved detection accuracy greater than 99% and reduced the list of features by 35%. The new system exhibits good generalization capability, botnet attack resistance, and applicability in high-dimensional applications. The results show a good future for practical application in real-time intrusion detection on IoTsReferences
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https://doi.org/10.31449/inf.v49i11.8708Downloads
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