Open AccessEPCAD: Efficient and Privacy-Preserving Data Anomaly Detection Scheme for Industrial Control System Networks
Author(s) -
Hanjun Gao,
Yuan Liu,
Fanglong Yin,
Gang Shen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1856/1/012028
Subject(s) - scheme (mathematics) , homomorphic encryption , computer science , anomaly detection , cryptosystem , support vector machine , the internet , data mining , controller (irrigation) , industrial control system , control (management) , computer security , encryption , artificial intelligence , operating system , mathematics , mathematical analysis , biology , agronomy
With the integration of Internet and industry, traditional industrial control system (ICS) has faced cyber-security risks and challenges due to interacting with the Internet. In this paper, we propose an efficient and privacy-preserving data anomaly detection scheme (EPCAD) for ICS. The scheme, a combination of a homomorphic cryptosystem and the support vector machine (SVM) algorithm, has the capability of efficiently detecting anomalies in data without compromising data information. Security analysis result shows that the EPCAD scheme has the following advantages: protect the data in the programmable logic controller (PLC); ensure that the classification parameters of anomaly detection server (ADS) are not compromised. Performance evaluation analysis demonstrates that the EPCAD scheme has significant advantages in terms of computational costs and communication overheads.