Securing the Internet of Things: A Machine Learning Approach to Mitigate DoS Threats with an Intrusion Detection System
Keywords:
Internet of Things, Cross-Layer IDS, DoS attacks, Hybrid IDS, Machine LearningAbstract
This study addresses the threat of Denial of Service (DoS) attacks within the Internet of Things (IoT) and introduces a Hybrid Intrusion Detection System (IDS) designed for detecting Cross-Layer DoS assaults. Comparative analysis with a single IDS reveals a substantial reduction in false positive rates. The Hybrid IDS integrates various machine learning algorithms to prevent overfitting or underfitting, functioning in two stages—Anomaly detection and Signature detection. The initial stage (Anomaly Detection) produces an Output of First Stage which becomes input to the Second Stage (Signature Detection). The Output of the Second Stage gives the final attack classes. Notably, the study creates an adapted dataset by simulating multiple assault environment in the NetSim Simulator, emphasizing the concurrent selection of the best feature set and critical feature using an innovative technique. Additionally, the research includes a comparative analysis of testing datasets under varying attacker nodes, network nodes, and processing time efficiency scenarios. This further validates the proposed Hybrid IDS's effectiveness in mitigating DoS attacks in the IoT.
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