Neural Networks and Deep Learning Models in Intrusion Detection Systems

Intrusion Detection Systems (IDS) are critical for safeguarding modern networks against increasingly sophisticated cyber threats. Traditional IDS approaches, often signature-based, struggle to detect novel or evolving attacks, highlighting the need for intelligent, adaptive mechanisms. Neural networks and deep learning models have emerged as promising solutions due to their ability to learn complex patterns and generalize from large datasets. This study explores the integration of neural network architectures—including Multi-Layer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks—into IDS frameworks to enhance detection accuracy and reduce false positives. We provide a comprehensive analysis of supervised, unsupervised, and hybrid learning approaches, examining their performance in identifying diverse attack types such as Denial-of-Service (DoS), probe attacks, and insider threats. The study also addresses challenges in applying deep learning to IDS, including data imbalance, high dimensionality, feature selection, and real-time processing constraints. Comparative evaluations demonstrate that deep learning-based IDS consistently outperform traditional machine learning methods, particularly in detecting zero-day attacks and complex multi-stage intrusions. Additionally, we discuss the role of autoencoders, Generative Adversarial Networks (GANs), and reinforcement learning in enhancing IDS adaptability and resilience. The findings underscore the potential of deep learning to transform IDS into proactive, intelligent security solutions capable of continuous learning and adaptation in dynamic network environments. Future research directions include optimizing model interpretability, reducing computational overhead, and developing standardized benchmarks to evaluate deep learning-based IDS performance across heterogeneous network scenarios.