Intrusion Detection System For IoT Networks Using Neural Networks With Extended Kalman Filter

Although coined in 1999, Internet of Things (IoT) has been one of the most sought-after technologies since the 1980s. However, with its remarkable growth comes the need to protect it against highly sophisticated cyberattacks. Acknowledging the fact that such attacks are not totally avoidable, early detection becomes essential. Over the years, the field of Machine Learning has been explored in detecting various network-based attacks. Hence, we take it into our account for creating an intelligent Intrusion Detection System (IDS) for IoT networks using a Neural Network with Extended Kalman Filter (EKF). The proposed system has been evaluated using two datasets, NSL-KDD and BoT-IoT datasets. The proposed system has been analyzed using several metrics such as accuracy, detection rate, and false negative rates.