Securing IoT and SDN systems using deep-learning based automatic intrusion detection

Both Internet of Things (IoT) and Software Defined Networks (SDN) have a major role in increasing efficiency and productivity for smart cities. Despite that, they face potential security threats that need to be reduced. A new Intrusion Detection System (IDS) has become necessary to secure them. Many researchers have recently used recent techniques such as machine learning to analyze and identify the rapid growth of attacks and abnormal behavior. Most of these techniques have low accuracy and less scalability. To address this issue, this paper proposes a Secured Automatic Two-level Intrusion Detection System (SATIDS) based on an improved Long Short-Term Memory (LSTM) network. The proposed system differentiates between attack and benign traffic, identifies the attack category, and defines the type of sub-attack with high performance. To prove the efficiency of the proposed system, it was trained and evaluated using two of the most recent realistic datasets; ToN-IoT and InSDN datasets. Its performance was analyzed and compared to other IDSs. The experimental results show that the proposed system outperforms others in detecting many types of attacks. It achieves 96.35 % accuracy, 96 % detection rate, and 98.4 % precision for ToN-IoT dataset. For InSDN dataset, the results were 99.73 % accuracy, 98.6 % detection rate, and 98.9 % precision.