Detection of cyber attacks on IoT based cyber physical systems

Abstract

The integration of Internet of Things (IoT) devices in Cyber- Physical Systems (CPS) continues to proliferate, ensuring the security of these interconnected systems becomes paramount. In existing research work focuses on the development and implementation of a cyber attack detection system for IoT-based CPS, leveraging Support Vector Machine (SVM) models. The SVM model, known for its effectiveness in binary classification tasks, is trained on historical data to distinguish between normal and malicious behavior patterns exhibited by IoT devices within the CPS. The SVM model is trained to learn the normal behavior of the system, enabling it to identify deviations indicative of cyber attacks. Realworld experiments and simulations demonstrate the efficacy of the SVMbased detection system in identifying various types of cyber threats. However, this research also acknowledges certain limitations. The SVM model's performance may be impacted by the dynamic and evolving nature of cyber threats, as it relies heavily on historical data for training and detection accuracy issues. To address the limitations of present cyber threat detection model , in this research work proposed a novel deep learning based CNN Model. The proposed model improve cyber attacks detection and performance metrics. The proposed model outperforms with the comparison of previous model. The performance measured in terms of accuracy, precision, recall and f1-score.