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Title: Anomaly-based intrusion detection for SCADA systems

Conference ·
OSTI ID:22030013
; ;  [1]
  1. Dept. of Nuclear Engineering, Univ. of Tennessee, Knoxville, TN 37996 (United States)

Most critical infrastructure such as chemical processing plants, electrical generation and distribution networks, and gas distribution is monitored and controlled by Supervisory Control and Data Acquisition Systems (SCADA. These systems have been the focus of increased security and there are concerns that they could be the target of international terrorists. With the constantly growing number of internet related computer attacks, there is evidence that our critical infrastructure may also be vulnerable. Researchers estimate that malicious online actions may cause $75 billion at 2007. One of the interesting countermeasures for enhancing information system security is called intrusion detection. This paper will briefly discuss the history of research in intrusion detection techniques and introduce the two basic detection approaches: signature detection and anomaly detection. Finally, it presents the application of techniques developed for monitoring critical process systems, such as nuclear power plants, to anomaly intrusion detection. The method uses an auto-associative kernel regression (AAKR) model coupled with the statistical probability ratio test (SPRT) and applied to a simulated SCADA system. The results show that these methods can be generally used to detect a variety of common attacks. (authors)

Research Organization:
American Nuclear Society, 555 North Kensington Avenue, La Grange Park, IL 60526 (United States)
OSTI ID:
22030013
Resource Relation:
Conference: NPIC and HMIT 2006: 5. International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology, Albuquerque, NM (United States), 12-16 Nov 2006; Other Information: Country of input: France; 12 refs.; Related Information: In: Proceedings of the 5. International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology| 1430 p.
Country of Publication:
United States
Language:
English