Deep Learning Model for Intrusion Detection in SDN Networks

Network security has grown in importance as computer network technology has progressed. Today networking technologies are vulnerable to a variety of security risks, and intrusion detection has emerged as a key tool for identifying network attacks. The architecture of Software-Defined Networks (SDN) is utilized to offer network monitoring and function observation. In order to protect computer networks and systems, IDS serves a crucial role. In order to keep the SDN’s security at a high level, an IDS is typically created to monitor the SDN’s normal traffic. A SDN network’s different security threats can be detected and managed using a number of strategies. For the purpose to identify the attacks, we combine LSTM with a deep convolution neural network (Deep CNN) driven by Grey Wolf Optimization (GWO). Effective intrusion detection requires a CNN feature extraction procedure, deep LSTM is used to detect network intrusion, and a deep CNN-LSTM is trained with a specially devised optimization strategy to improve detection performance.