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Studying Analysts’ Data Triage Operations in Cyber Defense Situational Analysis

  • Chen ZhongEmail author
  • John Yen
  • Peng Liu
  • Rob F. Erbacher
  • Christopher Garneau
  • Bo Chen
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10030)

Abstract

Cyber defense analysts are playing a critical role in Security Operations Centers (SOCs) to make sense of the immense amount of network monitoring data for detecting and responding to cyber attacks, including large-scale cyber attack campaigns involving advanced persistent threats. The network data continuously generated by multiple cyber defense systems, which may contain many false alerts, are overwhelming to the analysts. Analysts often need to make quick decisions/responses in a very short time based on their awareness of the situation at that moment. Data triage is the first and the most fundamental step performed routinely by the analysts — it filters a massive network monitoring data to identify known malicious events. Due to the high noise-to-signal ratio of network monitoring data, this steps accounts for a very significant portion of the time and attention of intrusion detection analysts. Therefore, a smart human-machine system that improves the performance of data triage operation in SOC is highly desirable. In this chapter, we describe a human-centered smart data triage system that leverages the cognitive trace of intrusion detection analysts. Our approach is based on a dynamic cyber-human system that integrates three dimensions: cyber defense analysts, network monitoring data, and attack activities. The approach leverages recorded analytic processes of intrusion detection analysts, which we refer to as “cognitive traces”. These traces of the analysts capture the examples of malicious events detected from the network monitoring data. Such traces from senior analysts provide a powerful opportunity for training junior analysts in performing data triage operations. To realize this potential, we also developed a smart retrieval framework that automatically retrieves traces of other senior analysts based on their similarity to the events already identified by a junior analyst. The traces from analysts, as demonstrated by a case study, also enable us to better understand their analytic processes in a systematic, yet minimum-reactive way. We summarize this chapter by discussing limitations of the proposed framework and the directions of future research regarding improving the data triage operations of cyber defense analysts.

Notes

Acknowledgements

This work was supported by ARO W911NF-09-1-0525 (MURI), ARO W911NF-15-1-0576, NSF CNS-1422594, and NIETP CAE Cybersecurity Grant (BAA-003-15).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Chen Zhong
    • 3
    Email author
  • John Yen
    • 1
  • Peng Liu
    • 1
  • Rob F. Erbacher
    • 2
  • Christopher Garneau
    • 2
  • Bo Chen
    • 4
  1. 1.College of Information Sciences and TechnologyPennsylvania State UniversityState CollegeUSA
  2. 2.Army Research LabAdelphiUSA
  3. 3.Indiana University KokomoKokomoUSA
  4. 4.University of MemphisMemphisUSA

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