A High-Performance Deep Learning-Based Intrusion Detection System for Accurate Identification of Sophisticated Cyberattacks in Modern Network Environments

Abstract

Purpose: The purpose of this study is to design a reliable and high-performance intrusion detection system (IDS) that can effectively identify various sophisticated cyber-attacks in network environments using a hybrid deep learning approach. Design/Methodology/Approach: A descriptive and experimental research methodology was used based on the UNSW NB15 benchmark dataset, which includes real and synthetic network data and various types of attacks. Data preprocessing includes handling missing values, encoding features, normalisation, and selecting features for dimensionality reduction. The performance of the models is evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Research Limitation: The study is limited to experiments conducted on the UNSW-NB15 dataset, and real-time deployment constraints such as computational overhead and resource limitations were not extensively evaluated. Findings: The experimental results show that the hybrid deep learning architecture outperforms individual models in network intrusion detection. The hybrid model combining CNN, LSTM, and GRU achieved near-perfect accuracy in network intrusion detection, with extremely low false-positive and false-negative rates. The performance of recurrent models, such as LSTM, is superior in identifying network intrusion patterns, and the hybrid model performs best. Practical Implication: The proposed hybrid IDS framework can be effectively used in real-world network infrastructures to improve proactive threat detection, minimise false negatives, and enhance cybersecurity defences against evolving attack patterns. Social Implication: Improved intrusion detection systems help create a safer digital ecosystem by ensuring data safety, service availability, and trust in services delivered through networks, which is important for modern society. Originality / Value: The current research provides a comprehensive hybrid deep learning framework for intrusion detection that leverages both feedforward and recurrent neural networks. It emphasises the power of fusion models in developing accurate and reliable intrusion detection systems, making it a valuable contribution for researchers and practitioners in this field.