scholarly journals A Novel Covert Agent for Stealthy Attacks on Industrial Control Systems Using Least Squares Support Vector Regression

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Weize Li ◽  
Lun Xie ◽  
Zhiliang Wang
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Time Window ◽  
Support Vector ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Common Assumption ◽  
Sensory Data ◽  
Physical Plant ◽  
Stealthy Attacks

Research on stealthiness has become an important topic in the field of data integrity (DI) attacks. To construct stealthy DI attacks, a common assumption in most related studies is that attackers have prior model knowledge of physical systems. In this paper, such assumption is relaxed and a covert agent is proposed based on the least squares support vector regression (LSSVR). By estimating a plant model from control and sensory data, the LSSVR-based covert agent can closely imitate the behavior of the physical plant. Then, the covert agent is used to construct a covert loop, which can keep the controller’s input and output both stealthy over a finite time window. Experiments have been carried out to show the effectiveness of the proposed method.

The Quality Prediction in Small-Batch Producing Process Based on Weighted Least Squares Support Vector Regression

2012 ◽  
Vol 542-543 ◽  
pp. 411-415
Author(s):  
Chao Yong Yan ◽  
Yao Jun Yu
Keyword(s):  
Least Squares ◽  
Support Vector Regression ◽  
Time Window ◽  
Learning Algorithm ◽  
Weighted Least Squares ◽  
Prediction Method ◽  
Machining Process ◽  
Support Vector ◽  
Quality Prediction ◽  
Outer Race

A novel quality prediction method with mobile time window is proposed for small-batch producing process based on weighted least squares support vector regression (LS-SVR). The design steps and learning algorithm are also addressed. In the method, weighted LS-SVR is taken as the intelligent kernel, with which the small-batch learning is solved well and the nearer sample is set a larger weight, while the farther is set the smaller weight in the history data. A typical machining process of cutting bearing outer race is carried out and the real measured data are used to contrast experiment. The experimental results demonstrate that the prediction accuracy of the weighted LS-SVR based model is only 20%-30% that of the standard LS-SVR based one in the same condition. It provides a better candidate for quality prediction of small-batch producing process.

scholarly journals Detection of cyber-attacks in systems with distributed control based on support vector regression

2020 ◽  
Vol 12 (2) ◽  
pp. 104-109
Author(s):  
Dušan Nedeljković ◽  
Živana Jakovljević ◽  
Zoran Miljković ◽  
Miroslav Pajić
Keyword(s):  
Support Vector Regression ◽  
Cyber Attacks ◽  
Support Vector ◽  
Industrial Control ◽  
Industrial Control Systems ◽  
Industrial Plants ◽  
Physical Systems ◽  
Industrial Iot ◽  
Sensor Signals ◽  
Opening Up

Concept of Industry 4.0 and implementation of Cyber Physical Systems (CPS) and Internet of Things (IoT) in industrial plants are changing the way we manufacture. Introduction of industrial IoT leads to ubiquitous communication (usually wireless) between devices in industrial control systems, thus introducing numerous security concerns and opening up wide space for potential malicious threats and attacks. As a consequence of various cyber-attacks, fatal failures can occur on system parts or the system as a whole. Therefore, security mechanisms must be developed to provide sufficient resilience to cyber-attacks and keep the system safe and protected. In this paper we present a method for detection of attacks on sensor signals, based on e insensitive support vector regression (e-SVR). The method is implemented on publicly available data obtained from Secure Water Treatment (SWaT) testbed as well as on a real-world continuous time controlled electro-pneumatic positioning system. In both cases, the method successfully detected all considered attacks (without false positives).

scholarly journals Stream Data Load Prediction for Resource Scaling Using Online Support Vector Regression

Algorithms ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 37 ◽  
Author(s):  
Zhigang Hu ◽  
Hui Kang ◽  
Meiguang Zheng
Keyword(s):  
Support Vector Regression ◽  
Real Time ◽  
Virtual Machines ◽  
Time Window ◽  
Performance Model ◽  
Streaming Data ◽  
Support Vector ◽  
Load Prediction ◽  
Stream Data ◽  
Online Support Vector Regression

A distributed data stream processing system handles real-time, changeable and sudden streaming data load. Its elastic resource allocation has become a fundamental and challenging problem with a fixed strategy that will result in waste of resources or a reduction in QoS (quality of service). Spark Streaming as an emerging system has been developed to process real time stream data analytics by using micro-batch approach. In this paper, first, we propose an improved SVR (support vector regression) based stream data load prediction scheme. Then, we design a spark-based maximum sustainable throughput of time window (MSTW) performance model to find the optimized number of virtual machines. Finally, we present a resource scaling algorithm TWRES (time window resource elasticity scaling algorithm) with MSTW constraint and streaming data load prediction. The evaluation results show that TWRES could improve resource utilization and mitigate SLA (service level agreement) violation.

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