NeuroAgent-X: A Self-Evolving Cognitive Agent for Securing Consumer IoT Systems against AI-enabled Anomalies and Adversarial Threats

The rapid expansion of consumer IoT systems, from smart homes to wearables, generates massive heterogeneous data streams while exposing new attack surfaces to AI-enabled threats. Traditional anomaly detection remains typically limited by unimodal learning, static models, centralized processing, and poor adaptability to concept drift. To overcome these challenges, we propose NeuroAgent-X, a self-evolving cognitive agent that integrates neuro-symbolic reasoning, attention-guided multi-modal fusion, and federated swarm intelligence. The framework features a dual-pathway encoder for structured and unstructured data, a reinforcement-driven self-evolution module with episodic and semantic memory, and selective parameter sharing for privacy-preserving distributed learning. Evaluations on SWaT, UCI HAR, and WADI datasets show superior anomaly detection (94.2% precision, 92.7% recall, 93.4% F1, 0.979 AUC) with sub-120ms latency across cloud, fog, and edge deployments. NeuroAgent-X also demonstrates resilience to concept drift (94% retention), zero-day attacks (89.2% F1), and adversarial perturbations (86.5% F1 under ϵ=0.1), outperforming state-of-the-art baselines. Furthermore, its federated design reduces communication overhead by 78% while preserving data locality. These results establish NeuroAgent-X as a scalable and robust paradigm for securing consumer IoT against adaptive, AI-enabled threats.