Enhancing intrusion detection systems: Innovative deep learning approaches using CNN, RNN, DBN and autoencoders for robust network security

The increasing sophistication of cyber threats poses significant challenges to network security. This makes effective intrusion detection system (IDS) more important than ever before. Conventional IDS methods, which often rely on signatures or rules it will struggle to keep up with its complex attacks and evolution. This thesis evaluates and analyze the performance of DL algorithms. They include convolutional neural networks (CNN), recurrent neural networks (RNN), deep belief networks (DBN), and Auto-encoder. Using the models, these models are trained and tested only on the NSL-set. KDD data, which is a widely accepted benchmark for evaluating IDS performance. Results show that the proposed deep learning approach significantly outperforms traditional methods, has a higher detection rate, reduce the false positive rate and the ability to identify both known and unknown intrusions. They leverage the strengths of CNN, RNN, DBN, and autoencoders. Doing this research Advances IDS capabilities by providing a robust and adaptable solution to enhance network security.