Hossein Faghih Aliabadi - MSC.Computer Networks, Faculty of Electricity, Computer and Advanced Technologies of Urmia University, Iran

In computer networks, introducing an intrusion detection system with high precision and accuracy is considered vital. In this article, a proposed model using a deep learning algorithm is presented and its results are analyzed. To evaluate the performance of this algorithm, NSL-KDD, CIC-IDS ۲۰۱۸, UNSW-NB۱۵ and MQTT datasets have been used. The evaluation criteria include precision, accuracy, F۱ score, and, readability. The new approach uses a hybrid algorithm that includes a convolutional neural network (CNN) to extract general features and long-short-term memory (LSTM) to extract periodic features that are in the form of a layer. are cross-connected, it is introduced to detect penetration. This algorithm showed the highest known accuracy of ۹۹% on the NSL-KDD dataset.  It has reached ۹۷% in all criteria in UNSW-NB۱۵, ۹۶% in all criteria in CIC-IDS ۲۰۱۸, and also, in MQTT for three abstraction levels of features, i.e. packet-based flow features, unidirectional flow, and The two-way flow has reached above ۹۷%, which shows the superiority of this algorithm.

Internet of Things, Intrusion Detection System, Hybrid system, Deep Learning Introduction