Fortifying Internet of Things Security: Employing Deep Learning for Privacy‐Preserving Data Transmission in Clustered Environments

ABSTRACT In the past few years, due to the massive growth of IoT‐related devices in an interconnected ecosystem, serious attacks like distributed denial of service (DDoS), spoofing, sinkhole, and ransomware attacks have been observed. These extend from data breaches and privacy violations to several other types of cyber‐attacks. Therefore, this paper proposed a novel type of clustering‐based Tree Hierarchical Deep Convolutional Neural Network (TH‐DCNN) model with Upgraded Human Evolutionary Optimization Algorithm (UHEOA) as an additional dimension for safeguarding the IoT from such attacks. It utilizes an Improved Soft‐K‐Means (IS‐K‐Means) algorithm to effectively cluster the IoT nodes in order to optimize resource utilization. The TH‐DCNN guarantees efficient security by way of effective malicious attack recognition, whereas UHEOA adapts model parameters to operate at its best. The proposed TH‐DCNN‐UHEOA framework is tested in a simulation environment implemented using Python with 500 IoT nodes on a 4000 × 3600 m terrain area for 7 h under random mobility, with broadcast transmission and node restriction. The proposed framework achieves outstanding improvements compared with the state‐of‐the‐art progress, including DNN‐CL‐IoT, Co‐FitDNN‐IoT, and CNN‐TSODE‐IoT. The proposed TH‐DCNN‐UHEOA achieves a packet delivery ratio (PDR) of up to 25.04%, a network lifetime (NLT) of up to 19.56%, and a detection accuracy of up to 26.76% higher compared with these baselines. All the parameters such as energy consumption, communication cost, throughput, PDR, NLT, energy consumption (EC), number of alive sensor nodes (NAN), accuracy, and number of dead sensor nodes (NDN) determine its efficiency, certifying the framework can repel malicious attacks like DDoS, spoofing, and sinkhole attacks, providing strong security to IoT systems.