Exploring the Synergy of GAN and CNN Models for Robust Intrusion Detection in Cyber Security

It is crucial to protect digital systems from cyberattacks in our ever-evolving digital world. Malicious behavior, viruses, and intrusions provide serious threats to both persons and organizations. Through the use of Machine Learning and Deep Learning techniques in Intrusion Detection System, cyber security area has made significant progress for addressing these threats. The existing state of Machine Learning and Deep Learning-based Intrusion Detection System and their contributions to cyber security are thoroughly examined in this review article. The importance of intrusion detection for protecting digital assets was explained at the start of the investigation. In the context of machine learning-based intrusion detection, traditional machine learning methods such as Support Vector Machine and Random Forests were investigated for anomaly detection. Objective of the paper is to show how deep learning has transform intrusion detection, with the power of deep learning methods like Convolutional Neural Network, Recurrent Neural Network, Deep Neural Network, Deep Belief Network, Restricted Boltzmann Machine and Auto Encoders. The use of Generative Adversarial Network was also examined to improve detection performance. The study concludes by emphasizing the necessity for continued research in Machine Learning and Deep Learning oriented intrusion detection and by focuses on the value of benchmark datasets and integration of many strategies to improve cybersecurity in our networked society. Researchers and professionals working to improve digital security against invasions might benefit greatly from it.