AI/ML-Driven Intrusion Detection and Threat Prediction for Intelligent Cybersecurity Management in Next-Generation Networks

Abstract

The rapid evolution of digital infrastructures and the exponential growth of connected devices have dramatically increased the surface area for cyber threats. Traditional security systems, largely rule-based and reactive, are struggling to combat increasingly sophisticated cyberattacks. Artificial Intelligence (AI) and Machine Learning (ML) are transforming the field of cybersecurity by enabling proactive, adaptive, and intelligent threat detection mechanisms. This paper presents an in-depth exploration of AI/ML-driven intrusion detection and threat prediction systems, highlighting their methodologies, models, challenges, and future prospects. The study emphasizes how AI-based systems enhance network resilience through real-time anomaly detection, pattern recognition, and predictive analytics. Additionally, this paper discusses various challenges such as data imbalance, adversarial attacks, explainability, and computational overhead while outlining future research directions to advance intelligent cybersecurity defense mechanisms.

KEYWORDS: Artificial Intelligence, Machine Learning, Intrusion Detection System, Threat Prediction, Cybersecurity, Anomaly Detection, Deep Learning, Network Security