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MAC: Multilevel Autonomous Clustering for Topologically Distributed Anomaly Detection

  • M. A. ParthaEmail author
  • C. V. Ponce
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 881)

Abstract

Anomaly detection in networks is an important cybersecurity threat detection capability. Anomalies in networks are often not localized to a single point, but are spread over a range of nodes. In this case of distributed anomalies, the anomalies are typically too subtle to detect at an individual-node level, and so require anomaly detection over groups of nodes. But it is usually not known a priori on which subset of nodes to focus, and it is infeasible to check all 2N subsets of nodes in a network. This renders distributed anomaly detection extremely challenging. An emerging strategy for detecting such anomalies is to apply a detection technique to a hierarchy of clusters of nodes in the network. However, developing such a hierarchy is challenging in large, decentralized networks with no central controller. In this work, we present Multilevel Autonomous Clustering (MAC), a novel local algorithm for self-organized, hierarchical clustering in distributed networks. MAC enables individual devices in a distributed system to determine their cluster membership at multiple levels using only local information, without centralized computation or information about the entire network. The result is an approach to hierarchical graph clustering that is both practical to use in large, real-world systems, as well as effective for distributed anomaly detection. The algorithm is evaluated on both synthetic and real-world networks. Its effectiveness for anomaly detection is demonstrated on various test problems.

Keywords

Graphs Anomaly detection Local clustering Hierarchical Distributed networks Decentralized algorithms 

Notes

Acknowledgements

Dr. Alyson Fox, Dr. Eisha Nathan, and Dr. Tim La Fond, of Lawrence Livermore National Laboratory (LLNL), provided significant feedback and suggestions for improving the MAC algorithm. Professor Marija Ilic (MIT LIDS, MIT Lincoln Laboratory) provided valuable support and guidance. Within LLNL, Global Security’s E Program, Critical Infrastructure Cybersecurity Group and Engineering’s CED, Computational Electromagnetics Group both provided programmatic support. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Lawrence Livermore National LaboratoryLivermoreUSA
  2. 2.Massachusetts Institute of TechnologyCambridgeUSA

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