RTCS: An Improved Real-Time Credibility-Based Intrusion Detection System

The Internet of Things (IoT) connects physical devices to the Internet via open communication protocols. Malicious actors can exploit vulnerabilities to steal data or manipulate critical IoT settings, so there is a need for strong security measures. We propose an improved real-time intrusion detection system (IDS) called the real-time credibility system (RTCS), which utilizes traffic statistics and authentication analysis to compute credibility. RTCS performs the authentication process by utilizing elliptic curve encryption and decryption operations, basic symmetric encryption, and hash functions. This process enables anonymous mutual authentication between IoT devices. Subsequently, RTCS accesses sparsified user history data and introduces flexibility in calculating user credibility by employing an adapted secondary paradigm combined with preset "tolerance parameters," which serve as optimal thresholds for classifying different users. When a normal user violates regulations, their credibility decreases by a specified degree. If a high-risk user commits another violation, RTCS cannot tolerate it, leading to a rapid decline in their credibility. RTCS implements diversion measures and provides assisted decision scores for different users. Experimental results demonstrate that our method achieves an F1-score of 0.9707 and an area under the curve score of 0.9535. Compared to other works, RTCS exhibits superior performance and proactivity.