ADVANCED INTRUSION DETECTION TECHNIQUES FOR NETWORK SECURITY ENGINEERS: A DEEP LEARNING APPROACH

Abstract : In the evolving landscape of cybersecurity, traditional intrusion detection systems (IDS) are increasingly challenged by the complexity, volume, and sophistication of modern cyber threats. To address these limitations, this study explores advanced intrusion detection techniques through the integration of deep learning models, offering network security engineers a powerful toolset to enhance detection accuracy and adaptability. This research delves into the application of various deep learning architectures—such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and hybrid models—for identifying anomalous behavior and malicious activity in network traffic. The primary focus is on leveraging deep learning’s capacity to autonomously extract high-level features from raw input data, thus eliminating the need for manual feature engineering and enabling the detection of zero-day attacks and previously unseen threat patterns. Using benchmark datasets such as NSL-KDD, CICIDS2017, and UNSW-NB15, this study evaluates the performance of proposed models in terms of precision, recall, F1-score, and accuracy, highlighting their superiority over conventional machine learning approaches. Additionally, we examine techniques for enhancing model robustness, including adversarial training, data augmentation, and transfer learning. Real-time detection capabilities are also addressed by implementing optimized architectures with reduced computational overhead, making them suitable for deployment in enterprise-level environments. The research further discusses challenges related to data imbalance, interpretability of deep learning decisions, and the integration of IDS with other security mechanisms such as firewalls and Security Information and Event Management (SIEM) systems. A comparative analysis is presented to provide insights into the trade-offs between detection accuracy and computational efficiency across different model types. Finally, recommendations for future research include the exploration of unsupervised and semi-supervised learning approaches, federated learning for decentralized data privacy, and the application of explainable AI (XAI) methods to enhance trust and transparency in decision-making. This comprehensive study serves as a practical guide and knowledge base for network security engineers seeking to harness the power of deep learning to build intelligent, scalable, and adaptive intrusion detection systems capable of defending against the dynamic threat landscape of modern networks.

Keyword Intrusion Detection Systems (IDS), Network Security, Deep Learning, Anomaly Detection, Cyber Threats, Convolutional Neural Networks (CNN)