Security-Aware IoT Attack Detection Framework

Intrusion detection is a crucial part of IoT security since the rapid expansion of Internet of Things (IoT) devices has led to significant vulnerabilities in security. Advanced and new cyber threats are often missed by traditional security measures. In order to increase detection accuracy, this research presents a hybrid intrusion detection system (IDS) that combines deep learning and machine learning algorithms. For lightweight detection, we employ Gaussian Naïve Bayes (GNB), AdaBoost, and Extra Tree Classifiers; for improved feature extraction and recognition, we employ deep learning models. Benchmark IoT datasets are used to test the proposed method, which shows outstanding detection accuracy, low false positive rates, and adaptability to new attack patterns. Our technique's effectiveness is confirmed by experimental results, making it a scalable and real-time solution for IoT security.