Automatic recognition of advanced persistent threat tactics for enterprise security

Qingtian Zou, Anoop Singhal, Xiaoyan Sun, Peng Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

Advanced Persistent Threats (APT) has become the concern of many enterprise networks. APT can remain undetected for a long time span and lead to undesirable consequences such as stealing of sensitive data, broken workflow, and so on. To achieve the attack goal, attackers usually leverage specific tactics that utilize a variety of techniques. This paper explores the recognition of APT tactics through synthesized analysis and correlation of data from various sources.We propose a framework for detecting the APT tactics and discuss the application of different APT technique identification methods. Our framework can be used by the security analysts for effective detection of APT attacks. The evaluation of our approach shows that it can detect APT tactics with high accuracy and low false positive rate. Therefore, it can be used for tactic-centric APT detection and effective implementation of cyber security response operations.

Original languageEnglish (US)
Title of host publicationIWSPA 2020 - Proceedings of the 6th International Workshop on Security and Privacy Analytics
PublisherAssociation for Computing Machinery, Inc
Pages43-52
Number of pages10
ISBN (Electronic)9781450371155
DOIs
StatePublished - Mar 16 2020
Event6th ACM International Workshop on Security and Privacy Analytics, IWSPA 2020 - New Orleans, United States
Duration: Mar 18 2020 → …

Publication series

NameIWSPA 2020 - Proceedings of the 6th International Workshop on Security and Privacy Analytics

Conference

Conference6th ACM International Workshop on Security and Privacy Analytics, IWSPA 2020
Country/TerritoryUnited States
CityNew Orleans
Period3/18/20 → …

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

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