Intrusion Detection System

Intrusion Detection Systems (IDS) aim to identify malicious activities within computer networks, primarily by analyzing network traffic patterns to distinguish between normal and anomalous behavior. Current research emphasizes improving IDS accuracy and reliability through advanced machine learning techniques, including deep learning models like convolutional neural networks (CNNs), recurrent neural networks (RNNs, such as LSTMs), and graph neural networks (GNNs), as well as ensemble methods and federated learning for enhanced privacy and scalability. The development of more robust, explainable, and efficient IDSs is crucial for securing increasingly complex and interconnected systems, impacting both cybersecurity practices and the broader field of machine learning.