Unraveled — A semi-synthetic dataset for Advanced Persistent Threats

Sowmya Myneni, Kritshekhar Jha, Abdulhakim Sabur, Garima Agrawal, Yuli Deng, Ankur Chowdhary, Dijiang Huang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Unraveled is a novel cybersecurity dataset capturing Advanced Persistent Threat (APT) attacks not available in the public domain. Existing cybersecurity datasets lack coherent information about sophisticated and persistent cyber-attack features, including attack planning and deployment, stealthiness of the attacker(s), longer dorm period between attack activities, etc. Our APT attack scenario in Unraveled is implemented on a real network system established on a cloud platform to emulate an organization's network system. The new dataset provides a comprehensive network flow and host-level log information about the normal user(s) traffic and the cyber attacks traffic. To emulate realistic network traffic scenarios, Unraveled also includes attacks at different skills reflecting a typical organization's threat posture, and by utilizing APT attack information from one of the well-known APT attack databases, i.e., MITRE's APT-group database. Furthermore, we design and develop an Employee Behavior Generation (EBG) model to emulate multiple normal employees’ traffic and activities during a 6-week time period based on their pre-defined business functions. Using well-known machine learning models for anomaly detection, we show that the APT attack activities in Unraveled are hardly detected, indicating the need for more effective solutions that are based on datasets representing real world APT attacks.

Original languageEnglish (US)
Article number109688
JournalComputer Networks
Volume227
DOIs
StatePublished - May 2023

Keywords

  • Advanced Persistent Threats
  • Dataset
  • Threat detection

ASJC Scopus subject areas

  • Computer Networks and Communications

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