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Detecting lateral movement: A systematic survey.

Christos Smiliotopoulos1, Georgios Kambourakis1, Constantinos Kolias2

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Summary
This summary is machine-generated.

This survey offers a comprehensive overview of identifying Lateral Movement (LM) using Intrusion Detection Systems (IDS), covering Endpoint Detection and Response (EDR), machine learning, and graph-based strategies.

Keywords:
Advanced persistent threatAttacksIoTLateral movementNetwork security

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Area of Science:

  • Cybersecurity
  • Network Intrusion Detection

Background:

  • Lateral Movement (LM) is crucial in cyber attack frameworks like MITRE ATT&CK.
  • Existing research lacks a comprehensive survey on LM identification from an Intrusion Detection System (IDS) perspective.

Purpose of the Study:

  • To provide a systematic and holistic overview of Lateral Movement identification using IDS.
  • To cover emerging paradigms like the Internet of Things (IoT) within the scope of LM detection.

Main Methods:

  • Conducted a systematic survey of 53 articles over an eight-year period.
  • Categorized existing research into three main focus areas: Endpoint Detection and Response (EDR) schemes, machine learning solutions, and graph-based strategies.

Main Results:

  • Identified key trends and interrelations in LM detection research.
  • Highlighted progress in EDR, machine learning, and graph-based approaches for IDS.

Conclusions:

  • The survey addresses a gap in understanding LM identification via IDS.
  • Key observations are provided to guide future research in advancing LM detection techniques.