HYBRID MACHINE LEARNING FRAMEWORK FOR BOTNET ATTACK DETECTION IN IOT NETWORKS

The goal of this research is to create a hybrid machine learning model that can quickly detect botnet assaults in Internet of Things (IoT) settings. As the number of Internet of Things (IoT) devices grows, networks are more vulnerable to botnets that take advantage of device flaws and send large volumes of unsolicited traffic. Because IoT networks are complex and always changing, traditional security measures often fail to detect these kinds of threats. In order to improve identification accuracy and lower the number of false positives, the suggested method integrates a number of machine learning approaches. By combining the best elements of several algorithms, the hybrid model expertly analyzes network traffic patterns, detects unusual behavior, and arranges botnet activities. Experiments on IoT network datasets show that the proposed system performs better than single-model techniques in terms of scalability, reliability, and detection. The results show that using a combination of machine learning approaches can improve the security of the Internet of Things (IoT) and help people defend against new botnet threats.