DEEP LEARNING-BASED SECURITY OF IoT NETWORKS

The Internet of Things (IoT) connects sensors, appliances, vehicles, healthcare equipment, and industrial systems to exchange data and support automated services. However, heterogeneous devices, limited computing resources, inconsistent updates, and continuous connectivity create security risks, including denial-of-service activity, malicious traffic, unauthorized access, and data exposure. This paper reviews selected research on deep-learning-based IoT security and organizes the findings into a conceptual layered framework. The reviewed directions include multiclass intrusion detection, trust-index-based identification of suspicious nodes, privacy-aware learning, resource-aware cryptographic optimization, and modular industrial IoT architecture. The framework includes secure telemetry collection, preprocessing, feature extraction, deep-learning inference, trust and policy evaluation, and alerting with audit logging. This is a literature-based study; no new model was trained and no experimental results are claimed. Practical value depends on representative data, low false-alarm rates, privacy safeguards, resource efficiency, and secure integration with conventional controls.