OPTIMIZED ALTERNATING GRAPH-REGULARIZED NEURAL NETWORK FOR CYBER SECURITY THREATS DETECTION IN INTERNET OF THINGS
Keywords:
Alternating Graph-regularized Neural Network, Edge-Aware Smoothing Sharpening Filtering, General Synchroextracting Chirplet Transformand Sea Horse Optimization AlgorithmAbstract
The Internet of Things, connects programmes, devices, data storage, and services that, since they are always offering services to the business, may create new opportunities for cyber-attacks. At the moment, there is a considerable risk of malware assaults and software piracy jeopardising IoT security. These dangers have potential to steal crucial information that harms company's finances, reputation. In this manuscript, Optimized Alternating Graph-regularized Neural Network for Cyber Security Threats Detection in IoT (AGRNN-CS-TD-IoT) is proposed. At first the input data gathered from Google Code Jam Dataset (GCJ). The collected input data is pre-processed using Edge-Aware Smoothing Sharpening Filtering (EASSF); then the processed data is feature extracted using General Synchroextracting Chirplet Transform (GSCT). Extracted features fed to Alternating Graph-Regularized Neural Network (AGRNN) for effectively classifying the Cyber security threats as Benign and Malicious. In general, Alternating Graph-Regularized Neural Network does not express adapting optimization strategies determine optimal parameters to ensure accurate cyber security threats. Hence, Sea Horse Optimization Algorithm (SHOA) utilized to optimize parameters of AGRNN to classify the cyber security threats more accurately. Then the proposed AGRNN-CS-TD-IoT is implemented in Python and performance metrics likes FI-measure, accuracy, error rate, precision, sensitivity, ROC and computational time are analysed. Performance of the AGRNN-CS-TD-IoT approach attains 22.52%, 29.40% and 21.02% higher Accuracy when analysed through existing techniques like Cyber Security Threats Detection in Internet of Things Utilizing Deep Learning Approach(CSD-IoT-DLA), Securing Critical Infrastructures: Deep-Learning-Based Threat Detection in IIoT (SCI-DLTD-IoT) and Edge-IIoT set: New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning(CSD-IoT-FL) methods respectively.
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