Self-healing AI-driven networks for automated cyber threat detection and recovery

Abstract

The rapid evolution of cyber threats, including zero-day exploits, ransomware, and distributed denial-of-service (DDoS) attacks, poses unprecedented risks to modern network infrastructures. Conventional defense mechanisms, while effective in certain contexts, are reactive and often dependent on human intervention, which limits their ability to mitigate fast-moving and adaptive attacks. This increasing complexity highlights the urgent need for intelligent, autonomous solutions capable of securing digital ecosystems while ensuring continuity of operations. This paper introduces a self-healing AI-driven network architecture designed to detect, contain, and recover from cyber threats in real time. At a broad level, the system integrates machine learning–based anomaly detection with automated root cause analysis to identify malicious behaviors with precision. Building on this foundation, the architecture deploys adaptive routing mechanisms and autonomous remediation protocols that dynamically reconfigure network pathways, isolate compromised nodes, and restore system integrity without requiring prolonged human oversight. By leveraging continuous learning, the model enhances its resilience over time, improving its ability to anticipate and counter novel attack strategies. Applied to scenarios involving zero-day exploits, ransomware, and large-scale DDoS events, the proposed framework demonstrates its ability to minimize downtime, prevent cascading failures, and maintain critical service delivery. Narrowing the focus, the system highlights the transformative role of self-healing mechanisms in reducing operational costs, strengthening organizational resilience, and setting new benchmarks for proactive cybersecurity. Ultimately, the work emphasizes that AI’s role is not limited to detection but extends to full-cycle recovery, positioning self-healing architectures as a cornerstone of next-generation secure networks.