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Anomaly Detection for Individual Sequences with Applications in Identifying Malicious Tools.

Shachar Siboni1, Asaf Cohen2

  • 1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

This study introduces a universal anomaly detection algorithm for time series data. It uses Lempel-Ziv compression to identify abnormal behavior without prior models, proving effective in cybersecurity and data analysis.

Keywords:
NYC taxi dataanomaly detectionbotnetscommand and control channelscomputer securityindividual sequenceslearningone-dimensional time seriesprobability assignmentstatistical modeluniversal compression

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Area of Science:

  • Computer Science
  • Data Science
  • Cybersecurity

Background:

  • Anomaly detection identifies abnormal behavior in data.
  • Traditional methods often require statistical models for normal data.
  • Some scenarios lack predefined models, necessitating model-free approaches.

Purpose of the Study:

  • To propose a universal anomaly detection algorithm for one-dimensional time series.
  • To develop a method that learns normal system behavior without prior assumptions.
  • To detect abnormalities in data without prior knowledge of anomalies.

Main Methods:

  • Utilizes information measures derived from the Lempel-Ziv (LZ) compression algorithm.
  • Optimally learns normal behavior during a learning phase.
  • Estimates the likelihood of new data and classifies it during operation.

Main Results:

  • Applied to computer security problems: Botnet Command and Control (C&C) detection, malicious tools detection, and data leakage identification.
  • Successfully applied to a benchmark anomaly detection dataset and NYC taxi data.
  • Demonstrates that attempts to fool the system by generating normal data are computationally bound to fail.

Conclusions:

  • The proposed algorithm offers a universal approach to anomaly detection in time series.
  • It effectively learns normal patterns and detects deviations without model assumptions.
  • Information-theoretic analysis confirms the robustness against adversarial attempts to mimic normal data.