Hybrid Efficient IDS Against Adversarial Attacks in IoT Networks

IoT services become more dominant as time passes and the growing security concerns become less relevant. Due to proliferating heterogeneity, emerging technologies, and resource-constrained IoT systems, intelligent systems are becoming more vulnerable to cyberattacks. As a result, practical solutions to security issues like privacy, scalability, authenticity, trust, and centralization are required. Since malicious actors frequently use obfuscation tactics to elude detection, traditional approaches to intrusion detection systems are no longer relevant. Furthermore, these methods fail to detect zero-day Attacks. Applying an intelligent mechanism based on machine learning or deep learning at a different stage is necessary to detect attacks. The purpose of selecting multiple algorithms is to satisfy the specific requirements of various users or groups. In other words, it is crucial to identify the most effective model for each sort of user. The suggested method consists of two stages: a signature-based IDS with a malicious signature already developed and an ML/DL algorithm trained using the IoT-23 public dataset. Results show that the proposed engine works better than existing cutting-edge methods, with an average accuracy of 95.3%.