Rooting Out Intruders Using Deep Learning Through RNN And Bilstm

Abstract

Protecting networks from harmful activity and illegal access is critical in the field of cybersecurity. As the first line of defense in this regard, intrusion detection systems (IDS) are essential. Distinguishing between benign and malevolent activity, however, is one of the major problems IDS faces, particularly in light of the growing complexity of cyberthreats. Although deep learning techniques show great potential, they are frequently plagued by overfitting, a phenomenon in which a model fits well on training data but does not generalize to new data. A unique framework that combines conventional machine learning methods with RNN and LSTM algorithms has been created to address this difficulty. When it comes to identifying temporal dependencies in network traffic, RNN and LSTM are especially useful. This is essential for identifying risks that are changing. The framework may identify malicious behaviour more accurately by fusing various neural network topologies with conventional machine learning techniques. Another important component of the framework is feature engineering, which is the process of identifying and modifying pertinent features from unprocessed data. This procedure enhances the IDS's overall accuracy by lowering false positives. A number of public datasets, including ISCX-IDS 2012, CICIDS2017, and CICIDS2018, have been used to thoroughly analyse the framework, showing its performance and efficiency.  The model delivers outstanding performance measures, such as an accuracy of 96.3%, Precision of 96.83%, and F1-score of 97.5%, through intensive training and validation. Its 98.1% recall rate demonstrates how well it works to reduce false negatives, which is important for early detection. It ensures network security and integrity by providing a more potent protection against the dynamic array of cyberthreats.