research-article

Analyzing Invariants in Cyber-Physical Systems using Latent Factor Regression

Authors:

Marjan Momtazpour,

Ratnesh Sharma,

Naren RamakrishnanAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 2009 - 2018

https://doi.org/10.1145/2783258.2788605

Published: 10 August 2015 Publication History

Abstract

The analysis of large scale data logged from complex cyber-physical systems, such as microgrids, often entails the discovery of invariants capturing functional as well as operational relationships underlying such large systems. We describe a latent factor approach to infer invariants underlying system variables and how we can leverage these relationships to monitor a cyber-physical system. In particular we illustrate how this approach helps rapidly identify outliers during system operation.

Supplementary Material

MP4 File (p2009.mp4)

Download
171.70 MB

References

[1]

Intel Lab Data. http://select.cs.cmu.edu/data/labapp3/index.html, 2008. {Online; accessed 03-June-2015}.

[2]

A. Abur and A. G. Exposito. Power system state estimation: Theory and implementation. Marcel Dekker, 2004.

[3]

L. Akoglu, H. Tong, and D. Koutra. Graph-based anomaly detection and description: A survey. Data Min. and Knowl. Disc., 28(4), 2014.

Digital Library

[4]

A. Arnold, Y. Liu, and N. Abe. Temporal causal modeling with graphical granger methods. In Proc of KDD'07, pages 66--75. ACM, 2007.

Digital Library

[5]

H. Chen et al. Exploiting local and global invariants for the management of large scale information systems. In Proc ICDM'08, pages 113--122, 2008.

Digital Library

[6]

H. Chen, G. Jiang, K. Yoshihira, and A. Saxena. Invariants based failure diagnosis in distributed computing systems. In Proc. IEEE Symp on Reliable Distributed Systems, pages 160--166, 2010.

Digital Library

[7]

Y. Chen, L. Xie, and P. Kumar. Dimensionality reduction and early event detection using online synchrophasor data. In Power and Energy Society General Meeting (PES), 2013 IEEE, pages 1--5, July 2013.

[8]

D. Cheng, M. T. Bahadori, and Y. Liu. Fblg: A simple and effective approach for temporal dependence discovery from time series data. In Proc KDD'14, pages 382--391, 2014.

Digital Library

[9]

J. C. F. De Winter and D. Dodou. Common factor analysis versus principal component analysis: a comparison of loadings by means of simulations. Communications in Statistics - Simulation and Computation, June 2014.

[10]

A. Deshpande, C. Guestrin, S. Madden, J. Hellerstein, and W. Hong. Model-driven data acquisition in sensor networks. In Proc. Int Conf on VLDB, 2004.

Digital Library

[11]

M. Ding, H. Chen, A. Sharma, K. Yoshihira, and G. Jiang. A data analytic engine towards self-management of cyber-physical systems. In Proc IEEE ICDCSW'13, pages 303--308, 2013.

Digital Library

[12]

Y. Ge, G. Jiang, M. Ding, and H. Xiong. Ranking metric anomaly in invariant networks. ACM Trans. Knowl. Discov. Data, 8(2):8:1--8:30, June 2014.

Digital Library

[13]

Y. Ge, G. Jiang, and Y. Ge. Efficient invariant search for distributed information systems. In Proc. ICDM'13, pages 1049--1054, 2013.

[14]

H. Harman. Modern Factor Analysis. University of Chicago Press, Chicago, 3rd ed. edition, 1976.

[15]

T. Ide, A. C. Lozano, N. Abe, and Y. Liu. Proximity-based anomaly detection using sparse structure learning. In Proc SDM'09, 2009.

[16]

G. Jiang, H. Chen, and K. Yoshihira. Discovering likely invariants of distributed transaction systems for autonomic system management. Cluster Computing, 9(4):385--399, Oct. 2006.

Digital Library

[17]

G. Jiang, H. Chen, and K. Yoshihira. Efficient and scalable algorithms for inferring likely invariants in distributed systems. IEEE Trans. on Knowl. and Data Eng., 19(11):1508--1523, Nov. 2007.

Digital Library

[18]

R. Jiang, R. Lu, Y. Wang, J. Luo, C. Shen, and X. S. Shen. Energy-theft detection issues for advanced metering infrastructure in smart grid. Tsinghua Science and Technology, 19(2):105--120, April 2014.

[19]

K. Joreskog. Some contributions to maximum likelihood factor analysis. Psychometrika, 38(2):183--202, 1967.

[20]

M. Khan, M. Li, P. Ashton, G. Taylor, and J. Liu. Big data analytics on pmu measurements. In Proc Int Conf on FSKD'14, pages 715--719, Aug 2014.

[21]

Y. Liu, A. Niculescu-mizil, A. C. Lozano, and Y. Lu. Learning temporal causal graphs for relational time-series analysis. In Proc ICML'10, pages 687--694, 2010.

[22]

D. Mashima and A. A. Cardenas. Evaluating electricity theft detectors in smart grid networks. In Research in Attacks, Intrusions, and Defenses, volume 7462 of Lecture Notes in Computer Science, pages 210--229. 2012.

Digital Library

[23]

M. Momtazpour, R. Sharma, and N. Ramakrishnan. An integrated data mining framework for analysis and prediction of battery characteristics. In Proc IEEE ISGT Asia'14, pages 774--779, 2014.

[24]

K. J. Preacher, G. Zhang, C. Kim, and G. Mels. Choosing the optimal number of factors in exploratory factor analysis: A model selection perspective. Multivariate Behavioral Research, 48:28--56, 2013.

[25]

I. Shafer, K. Ren, V. N. Boddeti, Y. Abe, G. R. Ganger, and C. Faloutsos. Rainmon: An integrated approach to mining bursty timeseries monitoring data. In Proc KDD'12, pages 1158--1166, 2012.

Digital Library

[26]

H. Shan, G. Jiang, and K. Yoshihira. Extracting overlay invariants of distributed systems for autonomic system management. In Proc IEEE Int Conf on Self-Adaptive and Self-Organizing Systems, pages 41--50, 2010.

Digital Library

[27]

A. Sharma, H. Chen, M. Ding, K. Yoshihira, and G. Jiang. Fault detection and localization in distributed systems using invariant relationships. In Proc IEEE Int Conf on DSN'13, pages 1--8, 2013.

Digital Library

[28]

A. B. Sharma, F. Ivancic, A. Niculescu-Mizil, H. Chen, and G. Jiang. Modeling and analytics for cyber-physical systems in the age of big data. SIGMETRICS Perform. Eval. Rev., 41(4):74--77, Apr. 2014.

Digital Library

[29]

D. Suhr. Principal component analysis vs. exploratory factor analysis. In Proceedings of SUGI 30, pages 203--30, 2005.

Cited By

Zhu QDing YJiang JYang S(2025)Anomaly detection using invariant rules in Industrial Control SystemsControl Engineering Practice10.1016/j.conengprac.2024.106164154(106164)Online publication date: Jan-2025
https://doi.org/10.1016/j.conengprac.2024.106164
Das ARatner DAiken A(2022)Performance Variability and Causality in Complex Systems2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOSC56246.2022.00021(19-24)Online publication date: Sep-2022
https://doi.org/10.1109/ACSOSC56246.2022.00021
Khadivi P(2020)Bag of Symbols for Time Series Distance Measurement and Applications2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378133(5088-5097)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378133
Show More Cited By

Index Terms

Analyzing Invariants in Cyber-Physical Systems using Latent Factor Regression
1. Information systems
  1. Information systems applications
    1. Data mining

Recommendations

illiad: InteLLigent Invariant and Anomaly Detection in Cyber-Physical Systems
Regular Papers and Special Issue: Urban Intelligence

Cyber-physical systems (CPSs) are today ubiquitous in urban environments. Such systems now serve as the backbone to numerous critical infrastructure applications, from smart grids to IoT installations. Scalable and seamless operation of such CPSs ...
Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining

Recent advances in AIoT technologies have led to an increasing popularity of utilizing machine learning algorithms to detect operational failures for cyber-physical systems (CPS). In its basic form, an anomaly detection module monitors the sensor ...
Deep Learning-based Anomaly Detection in Cyber-physical Systems: Progress and Opportunities

Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of CPSs and more sophisticated attacks, conventional anomaly detection methods, which face the growing volume of data and need ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2015

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

KDD '15

Sponsor:

KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

Acceptance Rates

KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Upcoming Conference

KDD '25

Sponsor:
sigkdd
sigkdd

The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 3 - 7, 2025

Toronto , ON , Canada

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
371
Total Downloads

Downloads (Last 12 months)6
Downloads (Last 6 weeks)1

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhu QDing YJiang JYang S(2025)Anomaly detection using invariant rules in Industrial Control SystemsControl Engineering Practice10.1016/j.conengprac.2024.106164154(106164)Online publication date: Jan-2025
https://doi.org/10.1016/j.conengprac.2024.106164
Das ARatner DAiken A(2022)Performance Variability and Causality in Complex Systems2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C)10.1109/ACSOSC56246.2022.00021(19-24)Online publication date: Sep-2022
https://doi.org/10.1109/ACSOSC56246.2022.00021
Khadivi P(2020)Bag of Symbols for Time Series Distance Measurement and Applications2020 IEEE International Conference on Big Data (Big Data)10.1109/BigData50022.2020.9378133(5088-5097)Online publication date: 10-Dec-2020
https://doi.org/10.1109/BigData50022.2020.9378133
Nakayama KMuralidhar NJin CSharma R(2019)Detection of False Data Injection Attacks in Cyber-Physical Systems using Dynamic Invariants2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA)10.1109/ICMLA.2019.00173(1023-1030)Online publication date: Dec-2019
https://doi.org/10.1109/ICMLA.2019.00173
Muralidhar NMuthiah SNakayama KSharma RRamakrishnan N(2019)Multivariate Long-Term State Forecasting in Cyber-Physical Systems: A Sequence to Sequence Approach2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9005511(543-552)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9005511
Chen HLi JCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Exploiting Structural and Temporal Evolution in Dynamic Link PredictionProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271740(427-436)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271740
Muralidhar NWang CSelf NMomtazpour MNakayama KSharma RRamakrishnan N(2018)illiadACM Transactions on Intelligent Systems and Technology10.1145/30661679:3(1-20)Online publication date: 29-Jan-2018
https://dl.acm.org/doi/10.1145/3066167
Khadivi PTandon RRamakrishnan N(2018)Flow of Information in Feed-Forward Denoising Neural Networks2018 IEEE 17th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC)10.1109/ICCI-CC.2018.8482098(166-173)Online publication date: Jul-2018
https://doi.org/10.1109/ICCI-CC.2018.8482098
Xun PZhu PHu YCui PZhang Y(2017)Command Disaggregation Attack and Mitigation in Industrial Internet of ThingsSensors10.3390/s1710240817:10(2408)Online publication date: 21-Oct-2017
https://doi.org/10.3390/s17102408
Vu HChai KKeating BTursynbek NXu BYang KYang XZhang ZLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Data Driven Chiller Plant Energy Optimization with Domain KnowledgeProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132860(1309-1317)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132860
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten