Malware Detection in Virtualization Environment | Detecting Process Injection in Virtualized Environment using VM Introspection and Memory Forensics

Darshan Tank

ISBN 10: 9999331343 / ISBN 13: 9789999331340

Published by Eliva Press, 2025

About this Item

Malware Detection in Virtualization Environment | Detecting Process Injection in Virtualized Environment using VM Introspection and Memory Forensics | Darshan Tank | Taschenbuch | Englisch | 2025 | Eliva Press | EAN 9789999331340 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Seller Inventory # 134576566

Bibliographic Details

• Title: Malware Detection in Virtualization Environment | Detecting Process Injection in Virtualized Environment using VM Introspection and Memory Forensics
• Publisher: Eliva Press
• Publication Date: 2025
• Language: English
• ISBN 10: 9999331343
• ISBN 13: 9789999331340
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 3D millimeters 229 millimeters width by 152 millimeters height by 3 millimeters depth
• Weight: 78 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Sophisticated malware frequently employs advanced evasion techniques to remain undetected by traditional security mechanisms. One of the most commonly used tactics is process injection, where malicious code is covertly inserted into the address space of legitimate processes. This allows the malware to operate under the guise of trusted applications, making detection significantly more challenging. In response to this issue, the present study introduces a novel detection methodology that functions entirely outside the virtual machine (out-of-VM). This technique leverages advanced memory introspection to identify and analyze different forms of process injection within virtualized environments. Notably, the approach is agentless, meaning it does not require any software to be installed within the guest VM, thereby eliminating the risk of the detection system itself being compromised or bypassed by the malware. Instead, it analyzes memory from the hypervisor level, providing a more secure and isolated vantage point. Experimental evaluations validate the effectiveness of the proposed method, demonstrating superior performance when compared to existing detection frameworks. Specifically, the method achieves higher detection accuracy, with more true positives and fewer false positives. It is capable of precisely identifying injected memory regions and detecting a broader spectrum of malware types, thereby outperforming current state-of-the-art solutions across all major evaluation metrics.