A Hybrid Intrusion Detection Approach for Cyber Attacks

Abstract

The field of cybersecurity constantly evolves as attackers develop new methods and technologies. Defending against cyberattacks involves a combination of robust security measures, regular updates, user education, and the use of advanced technologies, such as intrusion detection systems and artificial intelligence, to find out the threats in real-time. IDS are designed to identify and address any unauthorized actions or potential security threats within a computer network or system. A hybrid intrusion detection system (IDS) combines many detection techniques and strategies from different IDS types into a single, coherent solution. Combining the benefits of each approach should result in more comprehensive and effective intrusion detection. This paper outlines a proposed anomaly intrusion detection system (AIDS) framework that leverages a hybrid of deep learning strategies. It incorporates Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, which were developed using XGBoost, and their efficacy was assessed with the NSL-KDD dataset. The evaluation of the suggested model focused on its accuracy, detection capabilities, and the rate of false positives. The outcomes of this research are noteworthy within the cybersecurity field. In this paper, a framework of an Anomaly IDS is proposed. The purpose of an anomaly IDS, or AIDS, is to spot odd behavior on a network or system that might point to a security breach or malevolent attempt to hack it. Anomaly-based IDSs concentrate on finding departures from accepted typical behavior, in contrast to signature-based detection systems, which depend on a predefined database of known attack patterns.