Anomolous Behavioral Pattern Analysis for IoT Application Using Firewall

As Internet of Things (IoT) devices grow in many areas, such as smart homes, healthcare, business automation, and transportation, providing a security mechanism also becomes important. This brief presents a security solution that uses electronic technology to enhance IoT applications against various cyber threats. The solution begins by identifying the unique security challenges posed by the IoT ecosystem, including device diversity, access restrictions, and communication gaps. Recognizing the important role of firewalls in network security, our approach includes firewall resources designed for IoT environments. This article includes a behavioral analysis to identify negative behavioral patterns that indicate potential security hazards resulting in threat and response. It proposes a new concept that integrates machine learning algorithms with an electronic engine to crack code. Extensive experiments have been conducted to evaluate the effectiveness of the Framework. Using IoT traffic data from various applications. The results show that it is possible to identify bad patterns, including interference of illegal equipment, unreliable information, and poor communication.