Learning Based Intrusion Detection

Learning-based intrusion detection systems (IDS) leverage machine learning to automatically identify malicious network activity, aiming to improve upon traditional rule-based systems' limitations in detecting novel attacks. Current research emphasizes adapting these systems to diverse environments like the Internet of Things (IoT) and Internet of Vehicles (IoV), focusing on models such as convolutional neural networks, autoencoders, and ensemble methods, often incorporating techniques like federated learning and transfer learning to enhance efficiency and scalability. The development of robust and adaptable IDS is crucial for securing increasingly interconnected systems, impacting both cybersecurity practices and the broader scientific understanding of network security threats.