Test data for anomaly detection in the electricity infrastructure

This paper describes a large set of electricity data for the IEEE 24 bus system generated on a test bed that copies the cyber layer of the electricity infrastructure in some detail. This data has been filtered and corrupted with natural noise and a realistic set of failure-induced and attack-induced corruptions. One of the main applications of this data is the development of novel anomaly-detecting techniques, which could play a vital role in the identification and repair of problems in the cyber layer of the electricity infrastructure. To encourage work in this area, this data is made freely available online.

[1]  Barak A. Pearlmutter,et al.  Detecting intrusions using system calls: alternative data models , 1999, Proceedings of the 1999 IEEE Symposium on Security and Privacy (Cat. No.99CB36344).

[2]  R. Billinton,et al.  The IEEE Reliability Test System???Extensions to and Evaluation of the Generating System , 1986, IEEE Power Engineering Review.

[3]  Mohammad Shahidehpour,et al.  The IEEE Reliability Test System-1996. A report prepared by the Reliability Test System Task Force of the Application of Probability Methods Subcommittee , 1999 .

[4]  M Damashek,et al.  Gauging Similarity with n-Grams: Language-Independent Categorization of Text , 1995, Science.

[5]  A. P. Alves da Silva,et al.  Data debugging for real-time power system monitoring based on pattern analysis , 1996 .

[6]  Michael D. Ernst,et al.  Dynamically discovering likely program invariants , 2000 .

[7]  Stephanie Forrest,et al.  A sense of self for Unix processes , 1996, Proceedings 1996 IEEE Symposium on Security and Privacy.

[8]  Shyh-Jier Huang,et al.  Enhancement of power system data debugging using GSA-based data-mining technique , 2002 .

[9]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[10]  Giordano Vicoli,et al.  Novelty detection and management to safeguard information-intensive critical infrastructures , 2007 .

[11]  K. Clements,et al.  Detection and identification of topology errors in electric power systems , 1988 .

[12]  Ning Lu,et al.  Safeguarding SCADA Systems with Anomaly Detection , 2003, MMM-ACNS.

[13]  Xin Xu,et al.  A Reinforcement Learning Approach for Host-Based Intrusion Detection Using Sequences of System Calls , 2005, ICIC.

[14]  H.M. Khodr,et al.  Ant colony system algorithm for the planning of primary distribution circuits , 2004, IEEE Transactions on Power Systems.

[15]  John McHugh,et al.  Intrusion and intrusion detection , 2001, International Journal of Information Security.

[16]  John C. Munson,et al.  Watcher: the missing piece of the security puzzle , 2001, Seventeenth Annual Computer Security Applications Conference.

[17]  P. Helman,et al.  A formal framework for positive and negative detection schemes , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Salvatore J. Stolfo,et al.  Data Mining Approaches for Intrusion Detection , 1998, USENIX Security Symposium.

[19]  Simin Nadjm-Tehrani,et al.  Safeguarding Critical Infrastructures , 2004 .