Content area

Abstract

Malicious actors often exploit persistence mechanisms, such as unauthorized modifications to Windows startup directories or registry keys, to achieve privilege escalation and maintain access on compromised systems. While information technology (IT) teams legitimately use these AutoStart Extension Points (ASEPs), adversaries frequently deploy malicious binaries with non-standard naming conventions or execute files from transient directories (e.g., Temp or Public folders). This study proposes a threat-hunting framework using a custom Elasticsearch Security Information and Event Management (SIEM) system to detect such persistence tactics. Two hypothesis-driven investigations were conducted: the first focused on identifying unauthorized ASEP registry key modifications during user logon events, while the second targeted malicious Dynamic Link Library (DLL) injections within temporary directories. By correlating Sysmon event logs (e.g., registry key creation/modification and process creation events), the researchers identified attack chains involving sequential registry edits and malicious file executions. Analysis confirmed that Sysmon Event ID 12 (registry object creation) and Event ID 7 (DLL loading) provided critical forensic evidence for detecting these tactics. The findings underscore the efficacy of real-time event correlation in SIEM systems in disrupting adversarial workflows, enabling rapid mitigation through the removal of malicious entries. This approach advances proactive defense strategies against privilege escalation and persistence, emphasizing the need for granular monitoring of registry and filesystem activities in enterprise environments.

Details

1009240
Business indexing term
Title
Elasticsearch-Based Threat Hunting to Detect Privilege Escalation Using Registry Modification and Process Injection Attacks
Author
Bhardwaj Akashdeep 1   VIAFID ORCID Logo  ; Sapra Luxmi 2   VIAFID ORCID Logo  ; Rahman Shawon 3   VIAFID ORCID Logo 

 Centre for Cybersecurity, School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India 
 Faculty Computer Application, Graphic Era Hill University, Dehradun 248002, India; [email protected] 
 Department of Computer Science, University of Hawaii-Hilo, Hilo, HI 96720, USA 
Publication title
Volume
17
Issue
9
First page
394
Number of pages
28
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
19995903
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2025-08-29
Milestone dates
2025-07-08 (Received); 2025-08-23 (Accepted)
Publication history
 
 
   First posting date
29 Aug 2025
ProQuest document ID
3254515798
Document URL
https://www.proquest.com/scholarly-journals/elasticsearch-based-threat-hunting-detect/docview/3254515798/se-2?accountid=208611
Copyright
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2026-01-16
Database
ProQuest One Academic