The growing number of cyber threats and the implementation of stringent privacy regulations have revealed significant shortcomings in traditional centralized machine learning models, especially in distributed systems like the Internet of Things (IoT). This study presents a Federated Learning (FL) framework designed for intrusion detection and malware classification. This framework enables decentralized model training while preserving data locality and minimizing communication overhead. The proposed architecture incorporates lightweight, privacy-preserving techniques, including gradient clipping, differential privacy, and encrypted model aggregation, to ensure secure and efficient collaboration across heterogeneous clients. Experimental results on two widely adopted cybersecurity benchmarks demonstrate that the framework achieves detection accuracies above 90%, maintains privacy loss below 5%, and improves communication efficiency by over 25%. These results confirm the viability of FL as a scalable, privacy-compliant approach for next-generation cybersecurity systems in highly distributed infrastructures.

The increasing frequency of cyber threats and the enforcement of strict privacy regulations have exposed critical limitations of traditional centralized machine learning models, especially in distributed environments such as the Internet of Things (IoT). This study presents a federated learning (FL) framework tailored for intrusion detection and malware classification that enables decentralized model training while preserving data locality and minimizing communication overhead. The proposed architecture incorporates lightweight privacy-preserving techniques-including gradient clipping, differential privacy, and encrypted model aggregation-to ensure secure and efficient collaboration across heterogeneous clients. Experimental results on benchmark datasets, such as CICIDS2017 [1] and TON_IoT [2], show that the framework achieves detection accuracies above 90%, while maintaining privacy loss below 5% and improving communication efficiency by more than 25%. These results confirm the viability of federated learning as a scalable and privacy-compliant approach for next-generation cybersecurity systems in highly distributed infrastructures.

Cyber-physical infrastructures such as hospitals and smart campuses face hybrid threats that target both digital and physical domains. Traditional security solutions separate surveillance from network monitoring, leaving blind spots when attackers combine these vectors. This paper introduces ARGUS, an autonomous robotic platform designed to close this gap by correlating cyber and physical anomalies in real time. ARGUS integrates computer vision for facial and weapon detection with intrusion detection systems (Snort, Suricata) for monitoring malicious network activity. Operating through an edge-first microservice architecture, it ensures low latency and resilience without reliance on cloud services. Our evaluation covered five scenarios—access control, unauthorized entry, weapon detection, port scanning, and denial-of-service attacks—with each repeated ten times under varied conditions such as low light, occlusion, and crowding. Results show face recognition accuracy of 92.7% (500 samples), weapon detection accuracy of 89.3% (450 samples), and intrusion detection latency below one second, with minimal false positives. Audio analysis of high-risk sounds further enhanced situational awareness. Beyond performance, ARGUS addresses GDPR and ISO 27001 compliance and anticipates adversarial robustness. By unifying cyber and physical detection, ARGUS advances beyond state-of-the-art patrol robots, delivering comprehensive situational awareness and a practical path toward resilient, ethical robotic security.

While traditional surveillance and security methods remain widely used, they struggle to keep up with emerging threats. These systems lack autonomy, cannot process multimodal data in real time and are largely dependent on human intervention. This highlights the clear need for integrated solutions that combine autonomous robotic mobility, intelligent perception, and real-time contextual awareness, enabling faster and more effective responses to evolving security challenges. This paper presents ARGUS (Autonomous Robotic Guard System) an autonomous robotic platform for patrolling, detection, and response within cyber-physical environments. Unlike traditional systems, ARGUS operates independently, navigating, analyzing, and intervening in dynamic and potentially hazardous scenarios. The platform integrates advanced computer vision modules (facial recognition, vehicle, and bladed weapon detection), artificial intelligence for abnormal behavior assessment and contextual acoustic analysis, alongside embedded cyber protection capabilities through native Intrusion Detection Systems – IDS (Snort, Suricata). Additionally, ARGUS incorporates adaptive planning and routing algorithms (A*, D* Lite), Simultaneous Localization and Mapping (SLAM), and robust control strategies (backstepping, sliding-mode), enabling effective operation even in unstable, crowded, or unstructured environments. Beyond its autonomous functionalities, ARGUS can seamlessly integrate into a multi-agent network, coordinating real-time security responses with other mobile units and human operators. This paper details the system architecture, software and hardware components, implementation methodology, and testing scenarios, highlighting the advantages of adopting integrated robotic approaches for modern security applications.