Intrusion Detection System to detect impersonation attacks in IoT networks

The instant and rapid growth of interrelated communication and computing devices in the Internet which electronically binds the various systems in social and physical way and the raising levels in quantity of sensitive and significant data conveyed and transferred has posed a threat to concerns regarding the process of securing the privacy, safety in networks of devices of IoT. Upshot of the high count of devices being connected, they might be prone to cyber-attacks by illicit users. So there is always a need and demand for an excellent Intrusion Detection System (IDS) which identifies and prevents unauthorized access into the network system involving IoT devices. This proposed work presents Support Vector Machine (SVM) which is deployed and captures the network attributes from the given dataset for recognizing impersonation attack. We have utilized Aegean Wi-Fi Intrusion Dataset- (AWID), and experimental findings demonstrate that the performance and interpretation of the model proposed concerning to the detection rate, accuracy, and F1-score while identifying the impersonation attacks.