Authors: Prakash Gahora, Bhanu Pratap Singh
Abstract: The rapid growth of digital communication, cloud computing, Internet of Things (IoT), and smart infrastructures has significantly increased cybersecurity threats and network vulnerabilities. Traditional intrusion detection systems (IDS) often struggle to detect sophisticated and evolving cyber-attacks due to their dependence on static rule-based mechanisms. To address these limitations, Machine Learning (ML) and Explainable Artificial Intelligence (XAI) have emerged as promising solutions for intelligent and adaptive intrusion detection. This research explores the integration of ML and XAI techniques in intrusion detection systems to improve attack detection accuracy, transparency, and real-time threat response. The study reviews various machine learning approaches, including supervised learning, deep learning, reinforcement learning, and federated learning methods used in modern IDS frameworks. Additionally, the role of explainable AI in enhancing trust, interpretability, and decision-making within cybersecurity systems is examined. The proposed approach emphasizes intelligent threat detection, reduced false alarm rates, and improved adaptability in IoT, industrial, and distributed computing environments. The findings indicate that AI-driven IDS frameworks provide efficient and scalable cybersecurity solutions capable of addressing emerging cyber threats while ensuring transparency and reliability in security operations.
