IoT Maleware Detection Based On Anomaly Traffic Identification Using CNN-LSTM

IoT systems are widely used in modern times. However, they present a weak link in any network, and any infected device can seriously damage organizational networks. The security threats for IoT devices are increasing substantially with the advancement of processing power and the rise of AI. Different approaches are used to detect attacks, including machine learning approaches like SVM and RandomForest. However, the traffic volume, the accuracy, and the complexity of such approaches limit the power of detecting potential malware promptly. In this paper, we propose a CNN-LSTM approach to detect a list of known attacks as well as unknown ones Traffic behavior is a great source of information for detecting infected devices in a network. We used the IOT-23 dataset, which is an open dataset that contains more than 230 million records labeled with various attacks and normal traffic. The model proposed reached 96 % accuracy. The proposed method ensures faster detection of infected devices, thereby limiting the effect of infected devices on the network.