Leveraging Artificial Intelligence for Enhanced Threat Detection, Response, and Anomaly Identification in Resource-Constrained IoT Networks
Keywords:
Internet of Things (IoT), Artificial Intelligence (AI), Threat DetectionAbstract
The billion-device Internet of Things enables worldwide data collection, transmission, and analysis. Networked ecosystem weakens security. Hackable IoT devices have little memory and computational power. This evolving threat landscape defeats traditional security measures. AI's threat, response, and anomaly detection secure IoT networks.
AI's IoT ecosystem security is examined. AI-powered threat detection is investigated. Machine learning may detect sensor or network traffic issues. We employ supervised learning to categorize hazards and assist the system distinguish safe and harmful activities. We investigate how unsupervised learning algorithms may identify network patterns and security problems.
Beyond risk detection, AI-driven reactions are studied. AI-based incident response systems assess security events, trigger pre-defined countermeasures, and clean up in real time. AI-based self-healing improves network security. Protecting IoT networks involves anomaly detection. We explore AI network activity anomaly detection. Machine learning and statistical anomaly detection are evaluated in resource-limited settings.
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