AI-Driven Cybersecurity: Advancing Intelligent Threat Detection and Adaptive Network Security in the Era of Sophisticated Cyber Attacks

Abstract

The growing complexity of cyber threats has revealed the constraints of the old network security controls, which have led to the need to have more intelligent, dynamic, and proactive defense systems. Artificial Intelligence (AI) is a game-changing facilitator in cybersecurity that has provided superior means of real-time threat identification, anomaly detection and automatic reaction. The present study examines the concept of AI-based systems in the context of cybersecurity to identify how the technology can be used to enhance network security against new attack vectors. It analyzes machine learning and deep learning use in intrusion prevention and detection systems, emphasizes the creation of dynamic security models that can react to zero-day attacks, and analyzes the use of AI in enhancing zero-trust systems. Practical use in securing critical infrastructures, cloud environments, Internet of Things (IoT) ecosystems, and 5G networks are also discussed in the paper. Moreover, it compares the problem of adversarial AI, ethics, and the problem of model transparency that affects the reliability of intelligent security systems. This is based on the synthesis of the literature and case studies to highlight the promise of AI-driven cybersecurity in building resilient, scalable, and adaptive network defense mechanisms that are needed in the digital era.