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Abnormal network flow detection based on application execution patterns from Web of Things (WoT) platforms.

Young Yoon1, Hyunwoo Jung1, Hana Lee1

  • 1Department of Computer Engineering, Hongik University, Seoul, South Korea.

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This study introduces a novel method to detect abnormal network behaviors in Web of Things (WoT) applications using execution patterns. The RETE algorithm efficiently identifies deviations from normal network flow sequences, improving anomaly detection.

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Area of Science:

  • Computer Science
  • Network Security
  • Internet of Things

Background:

  • Web of Things (WoT) applications involve complex interactions between web services.
  • Identifying abnormal network behaviors in real-time is crucial for application security and reliability.
  • Existing methods may struggle with the dynamic nature of WoT application execution patterns.

Purpose of the Study:

  • To develop a novel methodology for identifying abnormal behaviors in WoT applications at the network monitor layer.
  • To leverage application execution patterns for anomaly detection.
  • To improve the effectiveness and efficiency of abnormal behavior detection for system administrators.

Main Methods:

  • Execution patterns of WoT applications were profiled as time sequences of web service invocation delays.
  • These execution patterns were converted into time sequences of network flows, forming a whitelist of valid behaviors.
  • A RETE algorithm was applied at the network monitor layer to compare runtime network flow sequences against the established whitelist.

Main Results:

  • The proposed method enables the interpretation of network flow sequences in the context of application logic.
  • Empirical evaluation demonstrated that the RETE-based algorithm effectively detects non-conforming network flow instances.
  • The RETE-based algorithm showed superior performance in terms of memory usage compared to a baseline algorithm.

Conclusions:

  • The developed methodology provides a robust approach for detecting abnormal behaviors in WoT applications.
  • Interpreting network flows using application execution patterns enhances the ability of administrators to identify and understand anomalies.
  • The RETE algorithm offers an efficient solution for real-time anomaly detection in WoT environments, particularly regarding memory footprint.