IADF-CPS: Intelligent Anomaly Detection Framework towards Cyber Physical Systems

Abstract

Cyber–Physical Systems (CPSs) becoming one of the most complex, intelligent, and sophisticated system. Ensuring security is an important aspect towards CPSs. However, increase in sophisticated and complexity attacks in CPSs, the conventional anomaly detection methods are facing problems and also growth in volume of data becomes challenging which requires domain specific knowledge that could be applied directly to analyze these challenges. In order to overcome this problem, various deep learning based anomaly detection system is developed. In this research, we propose an anomaly detection approach by integration of intelligent deep learning technique named Convolutional Neural Network (CNN) with Kalman Filter (KF) based Gaussian-Mixture Model (GMM). The proposed model is used for identifying and detecting anomalous behavior in CPSs. This proposed framework consists of two important process. First is to pre-process the data by transforming and filtering original data into new format and achieved privacy preservation of the data. Secondly, we proposed GMM-KF integrated deep CNN model for anomaly detection and accurately estimated the posterior probabilities of anomalous and legitimate events in CPSs.