Evaluating Neural Network Techniques in Intrusion Detection Systems: A Focus on CNN and Hybrid Strategies

Purpose of Study: The increasing focus on Artificial Intelligence (AI) worldwide has brought about potential benefits, but it also poses significant risks, especially in network security. Methodology: This study adopts a detailed comparative approach to evaluate the effectiveness of various Neural Network (NN) techniques in the context of Intrusion Detection Systems (IDS). The research focuses on four specific NN architectures: Artificial Neural Networks (ANN), Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). Each of these techniques is applied to different intrusion detection scenarios, using real-time network data. Result: The results indicate that different Neural Network techniques have varying levels of effectiveness in intrusion detection. Deep Neural Networks (DNN) demonstrated the highest accuracy in detecting complex threats, while Convolutional Neural Networks (CNN) were highly effective in pattern recognition within network traffic. Recurrent Neural Networks (RNN) performed well in temporal analysis of data, making them suitable for detecting persistent threats over time. Application of Study: The findings of this study are significant for the development of more sophisticated Intrusion Detection Systems (IDS) that can be applied in real-time network security environments. Novelty: The novelty of this study lies in its comprehensive analysis of multiple Neural Network techniques within the context of Intrusion Detection Systems (IDS). By comparing ANN, DNN, CNN, and RNN techniques, the study provides valuable insights into how AI can be leveraged to improve real-time network security.