Adam Improved Rider Optimization-Based Deep Recurrent Neural Network for the Intrusion Detection in Cyber Physical Systems

Abstract

This paper designed the intrusion detection systems for determining the intrusions. Here, Adam Improved rider optimization approach (Adam IROA) is newly developed for detecting the intrusion in intrusion detection. Accordingly, the training of DeepRNN is done by proposed Adam IROA, which is designed by combining the Adam optimization algorithm with IROA. Thus, the newly developed Adam IROA is applied for intrusion detection. Overall, two phases are included in the proposed intrusion detection system, which involves feature selection and classification. Here, the features selection is done using proposed WWIROA to select significant features from the input data. The proposed WWIROA is developed by combining WWO and IROA. The obtained features are fed to the classification module for discovering the intrusions present in the network. Here, the classification is progressed using Adam IROA-based DeepRNN. The proposed Adam IROA-based DeepRNN achieves maximal accuracy of 0.937, maximal sensitivity of 0.952, and maximal specificity of 0.908 based on SCADA dataset.

Automated Threat Detection Using Flamingo Search Algorithm With Optimal Deep Learning on Cyber-Physical System Environment

“…Cyberphysical systems (CPSs) embedded sensing, computing, control, and networking into physical components and frameworks, connecting them to the Internet and each other [3]. IDS is a foundational layer that should rapidly assess, detect, and respond to risky cyber traffic [4]. Network intrusion detection has been significant in detecting and monitoring possible attacks [5].…”