CASE STUDY: HYBRID CNN-LSTM WITH ATTENTION MECHANISM FOR INTRUSION DETECTION IN SDN ENVIRONMENTS

CASE STUDY: HYBRID CNN-LSTM WITH ATTENTION MECHANISM FOR INTRUSION DETECTION IN SDN ENVIRONMENTS

P. Loganayagi1, Dr. G. Ramesh2

1Information Technology, K.L.N College of Engineering

2Information Technology, K.L.N College of Engineering

Abstract - Network management is improved by Software-Defined Networking (SDN), which offers centralised control and programmability. Nevertheless, this centralisation also makes it susceptible to a number of assaults, such as brute force, botnet, infiltration, online attacks, Distributed Denial of Service (DDoS), and Denial of Service (DoS). In order to successfully detect and categorise these attacks, this paper suggests a hybrid deep learning model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks with an attention mechanism. To improve detection accuracy, the CNN component extracts spatial characteristics, the LSTM records temporal dependencies, and the attention mechanism highlights important features. The model's effectiveness in recognising various attack types in SDN environments is demonstrated by experimental assessments.

Key Words: SDN Security, Intrusion Detection, CNN-LSTM Hybrid, Attention Mechanism, Cyberattack Detection.