Threat Hunting the Shadows: Detecting Adversary Lateral Movement With Elasticsearch

This research investigates the elusive tactic of lateral movement employed by adversaries within a compromised network. The focus is on identifying the mechanisms and techniques used for lateral movement, with a particular emphasis on credential access. The study leverages a custom-designed Security Information and Event Management (SIEM) system built upon Elasticsearch, coupled with powerful KQL (Kibana Query Language) and Lucene search queries. Employing a realistic dataset, the research simulates an adversary’s TTPs (Tactics, Techniques, and Procedures) to dive deep into the critical area of credential access. This unique approach allows for the identification of indicators of compromise (IoCs) and the construction of targeted search queries to uncover signs and traces of lateral movement within the simulated environment. The findings contribute valuable insights into detection methodologies and highlight the effectiveness of an SIEM system in conjunction with advanced search functionalities for proactively countering lateral movement attempts.

Authors

Institutions

Publication Details

Journal
IEEE Access
Published
2025-01-01
DOI
https://doi.org/10.1109/access.2025.3556184
Citations
1
Primary Topic
Gait Recognition and Analysis
Type
article
Field-Weighted Citation Impact
0.67
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Threat Hunting the Shadows: Detecting Adversary Lateral Movement With Elasticsearch

Amr Jadi, Shoayee Dlaim Alotaibi, Talal Alshammari, Naif Alsharabi et al.
1 citations
IEEE Access
Gait Recognition and Analysis
0.67
article

Threat Hunting the Shadows: Detecting Adversary Lateral Movement With Elasticsearch

Amr Jadi, Shoayee Dlaim Alotaibi, Talal Alshammari, Naif Alsharabi, Akashdeep Bhardwaj, Dhahi Alshammari
article en
1 citations

Abstract

This research investigates the elusive tactic of lateral movement employed by adversaries within a compromised network. The focus is on identifying the mechanisms and techniques used for lateral movement, with a particular emphasis on credential access. The study leverages a custom-designed Security Information and Event Management (SIEM) system built upon Elasticsearch, coupled with powerful KQL (Kibana Query Language) and Lucene search queries. Employing a realistic dataset, the research simulates an adversary’s TTPs (Tactics, Techniques, and Procedures) to dive deep into the critical area of credential access. This unique approach allows for the identification of indicators of compromise (IoCs) and the construction of targeted search queries to uncover signs and traces of lateral movement within the simulated environment. The findings contribute valuable insights into detection methodologies and highlight the effectiveness of an SIEM system in conjunction with advanced search functionalities for proactively countering lateral movement attempts.

IEEE AccessVol. 13
University of Ha'il (SA), University of Petroleum and Energy Studies (IN)
Openalex Percentile: Top 33%
Gait Recognition and Analysis
0.67
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.