AI-Driven Cybersecurity: Intrusion Detection Using Deep Learning

Abstract

The escalating complexity of cyber threats necessitates advanced defense mechanisms that surpass the limitations of traditional rule-based intrusion detection systems. Recent advances in artificial intelligence, particularly deep learning, have transformed cybersecurity by enabling adaptive, data-driven models capable of detecting novel and sophisticated attacks. This paper explores the integration of deep learning into intrusion detection frameworks, highlighting its capacity to learn hierarchical feature representations from large-scale network traffic and system logs. Unlike conventional approaches, deep neural architectures such as convolutional and recurrent networks demonstrate resilience against zero-day exploits, evolving malware, and adversarial behaviors across critical infrastructures including smart manufacturing, grid modernization, and digital finance. Building on prior work in AI-enhanced security intelligence and threat modeling, we propose a conceptual framework that combines collaborative feature mapping, anomaly detection, and contextual threat classification to minimize false positives and improve response efficiency. Challenges such as data imbalance, model explainability, and real-time scalability are critically examined, along with opportunities for federated learning and hybrid AI human collaboration to address privacy and governance concerns. By synthesizing insights from recent research, this study underscores deep learning’s pivotal role in shaping next-generation intrusion detection systems and sets a trajectory for future inquiry in AI-driven cybersecurity.