Enhancing Cyber Security with Deep Learning and Intrusion Detection Systems

Abstract

Intrusion detection systems are effective in this research paper in several areas, specifically deep learning. An analysis of deep learning-based IDS techniques and their advantages and disadvantages are highlighted. Acknowledging the pivotal function of datasets in intrusion detection system development, we examine 35 prominent cyber security datasets. The targeted environment, which includes network traffic, electrical networks, internet traffic, virtual private networks, mobile apps, Internet-of-Things (IoT) traffic, and internet-connected devices, is the basis for categorizing these statistics. There is a comparison of seven deep learning architectures, including recurrent neural networks, convolutional neural networks, and autoencoders, to identify the best approach for maximizing intrusion detection accuracy and evaluate the effectiveness of various deep learning models, such as RNNs, CNNs, and autoencoders, in achieving optimal intrusion detection and assessment of each model's performance using two new real-world datasets in binary and multi-class classification tasks.