Automated IoT Device Identification Based on Full Packet Information Using Real-Time Network Traffic

In an Internet of Things (IoT) environment, a large volume potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity such sensitive data, it is important to initially verify source and its identity. Practically, IoT device identification primary step toward secure system. An appropriate approach can counteract malicious activities as sending false that trigger irreparable security issues in vital or emergency situations. Recent research indicates identity metrics Protocol (IP) Media Access Control (MAC) addresses are insufficient due their instability easy accessibility. Thus, identify device, analysis header information packets by imperative consideration. This paper proposes combination sensor measurement statistical feature sets addition set using classification-based framework. Various machine Learning algorithms have been adopted different combinations these provide enhanced devices. The proposed method has evaluated through normal under-attack circumstances collecting real-time devices connected lab setting show system robustness.