AI-Based Autonomous Cyber Defense Framework for Intelligent Threat Detection

Abstract: Cyber dangers have become more sophisticated and common in today's digital environment as a result of the spread of digital technologies and international networks of information systems. It is often difficult for traditional cybersecurity systems, particularly rule-based intrusion detection systems, to react quickly to intricate and dynamic cyberattacks. The application of machine learning (ML) and artificial intelligence (AI) methods to enhance cybersecurity threat detection has been the subject of recent research. However, the majority of current tactics lack an autonomous defense system that may respond to attacks on its own and are primarily focused on identifying cyber threats. In order to improve intelligent threat detection and response inside the existing digital infrastructures, this study proposes a conceptual design of an autonomous AI-driven cyber defense system. To reduce cyber risks, the proposed model will include automated response mechanisms, intelligent threat classification, and machine learning-based intrusion detection models. AI-based threat detection, threat classification, data gathering, data preprocessing, and feature extraction, as well as an autonomous defense response engine, form its foundation. The suggested framework can improve threat detection accuracy and reduce false positive rates, according to comparative research that used simulations and compared it with existing machine learning-based intrusion detection techniques. The suggested system can aid in the creation of intelligent cybersecurity systems that can progress and self-evolve to provide cyber protection in dynamic internet sources.

Keywords: Artificial Intelligence; Autonomous Cyber Defense; Intrusion Detection Systems (IDS); Machine Learning; Cyber Threat Detection; Cybersecurity Intelligence; Intelligent Security Systems.