Artificial Intelligence’s Growing Role in Cybersecurity

The rapid development of artificial intelligence (AI) has paved the manner for modern tactics to cybersecurity, especially in safeguarding industrial control systems (ICS) in opposition to sophisticated cyberattacks like ransomware.ICS are crucial to infrastructure sectors such as power grids, manufacturing, and water treatment but remain vulnerable to evolving ransomware attacks that disrupt operations and compromise security.Traditional ICS defenses, including signature-based malware detection, regularly fail to detect those dynamic threats, specifically zero-day ransomware, because of their reliance on static detection models and centralized data processing, which suffer from excessive latency and confined adaptability.This research introduces a hybrid AI-driven Intrusion Detection and Prevention System (IDPS) for ICS environments, leveraging federated learning, real-time anomaly detection, and adaptive threat classification.Unlike centralized models, federated learning allows non-stop training on distributed datasets without compromising sensitive data.The real-time anomaly detection layer complements the ability to detect unknown ransomware variants, while the adaptive classifier minimizes fake positives by refining the understanding of ICS-specific behaviors and protocols.The system was verified using a simulated ICS network with realistic attack situations and ICSspecific protocols such as Modbus and DNP3, attaining a detection accuracy of 96.5% and lowering ransomware detection latency by over 30% in comparison to present day centralized detection systems.These outcomes spotlight the ability of hybrid AI methodologies to offer robust, scalable, and privacy-preserving ransomware defenses tailored to the specific needs of critical ICS infrastructure, putting the level for future research on real-world deployment.