Enhancing Intrusion Detection Systems with Efficient Deep Learning Techniques

Abstract

In recent years, the proliferation of cyber threats has necessitated the development of robust intrusion detection systems (IDS). Traditional IDS methods often fall short in identifying sophisticated and evolving attacks. This research presents an innovative approach to IDS development using advanced deep learning techniques. By leveraging neural networks and machine learning algorithms, we aim to enhance the accuracy and efficiency of intrusion detection. Our model is designed to adapt and respond to new threat patterns in real-time, offering a significant improvement over conventional method. Through extensive experiments and evaluations, we demonstrate that our deep learning-based IDS not only reduces false positives but also achieves higher detection rates, ensuring a more secure and resilient network environment. Additionally, this paper presents a comparative analysis of binary and multi-class classification using the UNSW-NB15 dataset. For binary classification, a subset of 175,341 records with two target classes was used. The performance of the Convolutional Neural Network (CNN) model was evaluated using metrics such as precision, accuracy, and recall, yielding an accuracy of 81.07%, which outperforms the 79% accuracy obtained for multi-class classification. These results underscore the potential of deep learning to revolutionize cybersecurity and establish a new benchmark for future IDS development.