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Segmenting Large-Scale Cyber Attacks for Online Behavior Model Generation

  • Steven Strapp
  • Shanchieh Jay Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)

Abstract

Large-scale cyber attack traffic can present challenges to identify which packets are relevant and what attack behaviors are present. Existing works on Host or Flow Clustering attempt to group similar behaviors to expedite analysis, often phrasing the problem as offline unsupervised machine learning. This work proposes online processing to simultaneously segment traffic observables and generate attack behavior models that are relevant to a target. The goal is not just to aggregate similar attack behaviors, but to provide situational awareness by grouping relevant traffic that exhibits one or more behaviors around each asset. The seemingly clustering problem is recast as a supervised learning problem: classifying received traffic to the most likely attack model, and iteratively introducing new models to explain received traffic. A graph-based prior is defined to extract the macroscopic attack structure, which complements security-based features for classification. Malicious traffic captures from CAIDA are used to demonstrate the capability of the proposed attack segmentation and model generation (ASMG) process.

Keywords

Situational Awareness Closeness Centrality Attack Model Attack Behavior Destination Port 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Steven Strapp
    • 1
  • Shanchieh Jay Yang
    • 1
  1. 1.Department of Computer EngineeringRochester Institute of TechnologyRochesterUSA

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