HYBRID INTRUSION DETECTION FRAMEWORK FOR MOBILE EDGE COMPUTING

The growing use of mobile edge computing (MEC) has had a positive impact on user experience and reduced latency. However, this closeness also makes MEC environments vulnerable to a number of security risks. This research article presents an edge-based hybrid intrusion detection system for MEC and the Internet of Things (IoT). The system uses techniques like behavioral analysis, anomaly detection, and signature-based detection, ensuring real-time response and reduced bandwidth usage. The system also addresses challenges in data acquisition and cleaning due to potential threats from malicious users and noise. The model uses smoothing filters, unsupervised learning, and deep learning techniques to detect anomalies and threats, reducing bandwidth. According to the findings, securing MEC environments against changing cyber threats can be accomplished using an edge-based hybrid intrusion detection system.