Abascal, León; Rodríguez, Ricardo J.
Poster: Extracting Cryptographic Keys from Windows Live Processes Proceedings Article
In: Egele, Manuel; Moonsamy, Veelasha; Gruss, Daniel; Carminati, Michele (Ed.): Proceedings of the 22nd International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, pp. 213–219, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-97620-9.
Abstract | Links | BibTeX | Tags: cryptography, digital forensics, malware, Windows
@inproceedings{AbascalR-DIMVA-25,
title = {Poster: Extracting Cryptographic Keys from Windows Live Processes},
author = {León Abascal and Ricardo J. Rodríguez},
editor = {Manuel Egele and Veelasha Moonsamy and Daniel Gruss and Michele Carminati},
url = {https://webdiis.unizar.es/~ricardo/files/papers/AbascalR-DIMVA-25.pdf},
doi = {10.1007/978-3-031-97620-9_12},
isbn = {978-3-031-97620-9},
year = {2025},
date = {2025-01-01},
booktitle = {Proceedings of the 22nd International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment},
volume = {15748},
pages = {213–219},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Cryptographic keys are a fundamental aspect of modern system security, but when compromised, they become a critical vulnerability, especially in ransomware attacks. Paradoxically, these keys must be available in memory at runtime to function, creating a unique opportunity for defensive tools. We introduce nameTool, an open-source tool designed to locate cryptographic keys in active Windows processes using advanced memory analysis. Unlike traditional approaches that rely on static memory dumps, nameTool performs dynamic analysis in real time, restricting the search to process heap memory to improve efficiency and accuracy. It employs robust key identification heuristics to minimize false positives and is designed for seamless integration with Endpoint Detection and Response systems. nameTool also encourages extensibility: its open-source nature allows researchers and practitioners to enhance its capabilities with custom key detection algorithms. We validated our approach through extensive experiments involving both proof-of-concept ransomware and real-world samples, demonstrating the effectiveness of key extraction and decryption success. Our tool provides a practical path to strengthening ransomware mitigation strategies.},
keywords = {cryptography, digital forensics, malware, Windows},
pubstate = {published},
tppubtype = {inproceedings}
}
Cryptographic keys are a fundamental aspect of modern system security, but when compromised, they become a critical vulnerability, especially in ransomware attacks. Paradoxically, these keys must be available in memory at runtime to function, creating a unique opportunity for defensive tools. We introduce nameTool, an open-source tool designed to locate cryptographic keys in active Windows processes using advanced memory analysis. Unlike traditional approaches that rely on static memory dumps, nameTool performs dynamic analysis in real time, restricting the search to process heap memory to improve efficiency and accuracy. It employs robust key identification heuristics to minimize false positives and is designed for seamless integration with Endpoint Detection and Response systems. nameTool also encourages extensibility: its open-source nature allows researchers and practitioners to enhance its capabilities with custom key detection algorithms. We validated our approach through extensive experiments involving both proof-of-concept ransomware and real-world samples, demonstrating the effectiveness of key extraction and decryption success. Our tool provides a practical path to strengthening ransomware mitigation strategies.
Fernández-Álvarez, Pedro; Rodríguez, Ricardo J.
Module Extraction and DLL Hijacking Detection via Single or Multiple Memory Dumps Journal Article
In: Forensic Science International: Digital Investigation, vol. 44, pp. 301505, 2023, ISSN: 2666-2817, (Accepted for publication. To appear. Selected Papers of the Tenth Annual DFRWS Europe Conference).
Abstract | Links | BibTeX | Tags: digital forensics, DLL hijacking, memory forensics, Volatility, Windows
@article{FR-FSIDI-23,
title = {Module Extraction and DLL Hijacking Detection via Single or Multiple Memory Dumps},
author = {Pedro Fernández-Álvarez and Ricardo J. Rodríguez},
url = {http://webdiis.unizar.es/~ricardo/files/papers/FR-FSIDI-23.pdf},
doi = {10.1016/j.fsidi.2023.301505},
issn = {2666-2817},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Forensic Science International: Digital Investigation},
volume = {44},
pages = {301505},
abstract = {A memory dump contains the current state of a system's physical memory at the time of its acquisition. Among other things, it contains the processes that were running at the time of acquisition. These processes can share certain functionalities provided by shared object files, which are internally represented by modules in Windows. However, each process only maps in its address space the functionalities it needs, and not the entire shared object file. In this way, the current tools for extracting modules from existing processes in a memory dump from a Windows system obtain the partial content of the shared object files instead of the entire file. In this paper we present two tools, dubbed Modex and Intermodex, which are built on top of the Volatility 3 framework. These tools allow a forensic analyst to extract a 64-bit module from one or more Windows memory dumps as completely as possible. To achieve this, they aggregate the contents of the same module loaded by multiple processes that were running in the same memory dump or in different dumps (we called it intradump and interdump, respectively). Additionally, we also show how our developed tools are useful to detect dynamic-link library (DLL) hijacking attacks, a widely used attack on Windows where attackers trick processes into loading a malicious DLL instead of the benign one.},
note = {Accepted for publication. To appear. Selected Papers of the Tenth Annual DFRWS Europe Conference},
keywords = {digital forensics, DLL hijacking, memory forensics, Volatility, Windows},
pubstate = {published},
tppubtype = {article}
}
A memory dump contains the current state of a system's physical memory at the time of its acquisition. Among other things, it contains the processes that were running at the time of acquisition. These processes can share certain functionalities provided by shared object files, which are internally represented by modules in Windows. However, each process only maps in its address space the functionalities it needs, and not the entire shared object file. In this way, the current tools for extracting modules from existing processes in a memory dump from a Windows system obtain the partial content of the shared object files instead of the entire file. In this paper we present two tools, dubbed Modex and Intermodex, which are built on top of the Volatility 3 framework. These tools allow a forensic analyst to extract a 64-bit module from one or more Windows memory dumps as completely as possible. To achieve this, they aggregate the contents of the same module loaded by multiple processes that were running in the same memory dump or in different dumps (we called it intradump and interdump, respectively). Additionally, we also show how our developed tools are useful to detect dynamic-link library (DLL) hijacking attacks, a widely used attack on Windows where attackers trick processes into loading a malicious DLL instead of the benign one.
Fernández-Álvarez, Pedro; Rodríguez, Ricardo J
Extraction and Analysis of Retrievable Memory Artifacts from Windows Telegram Desktop Application Journal Article
In: Forensic Science International: Digital Investigation, vol. 40, pp. 301342, 2022, ISBN: 2666-2817.
Abstract | Links | BibTeX | Tags: digital forensics, instant messaging, memory forensics, Telegram Desktop, Windows
@article{FR-FSIDI-22,
title = {Extraction and Analysis of Retrievable Memory Artifacts from Windows Telegram Desktop Application},
author = {Pedro Fernández-Álvarez and Ricardo J Rodríguez },
url = {http://webdiis.unizar.es/~ricardo/files/papers/FR-FSIDI-22.pdf},
doi = {10.1016/j.fsidi.2022.301342},
isbn = {2666-2817},
year = {2022},
date = {2022-01-01},
journal = {Forensic Science International: Digital Investigation},
volume = {40},
pages = {301342},
abstract = {Instant messaging applications have become a very common way of communicating, and today there are many applications of this type. The forensic analysis of these applications can help provide essential clues to solve or clarify a possible crime. This type of applications generally store their data in a secure way or transmit it through encrypted channels and thus, the forensic analysis of memory takes on special relevance to analyze them. Following a three-phase forensic analysis methodology, this work has developed a forensic analysis environment for instant messaging applications composed of two tools. One of the tools is responsible for extracting the content of a process that runs on a Windows system, while the other focuses on studying the information present in the process memory of an instant messaging application. This second tool can be easily adapted and extended to provide analysis support for any instant messaging application. As a case study, we focus on the Telegram application for Windows systems called Telegram Desktop. Adapting these tools to this application, their joint use allows obtaining forensic artifacts of interest for an investigation, such as user contacts or the content of conversations that have taken place, among others, even when the application is blocked. Obtaining these data is of great help for a forensic analyst, since the analysis of these data can be vital to clarify the events that occurred in some type of criminal act. Both tools are open source under the GNU/GPLv3 license to promote their use and extensibility to applications of other instant messaging services.},
keywords = {digital forensics, instant messaging, memory forensics, Telegram Desktop, Windows},
pubstate = {published},
tppubtype = {article}
}
Instant messaging applications have become a very common way of communicating, and today there are many applications of this type. The forensic analysis of these applications can help provide essential clues to solve or clarify a possible crime. This type of applications generally store their data in a secure way or transmit it through encrypted channels and thus, the forensic analysis of memory takes on special relevance to analyze them. Following a three-phase forensic analysis methodology, this work has developed a forensic analysis environment for instant messaging applications composed of two tools. One of the tools is responsible for extracting the content of a process that runs on a Windows system, while the other focuses on studying the information present in the process memory of an instant messaging application. This second tool can be easily adapted and extended to provide analysis support for any instant messaging application. As a case study, we focus on the Telegram application for Windows systems called Telegram Desktop. Adapting these tools to this application, their joint use allows obtaining forensic artifacts of interest for an investigation, such as user contacts or the content of conversations that have taken place, among others, even when the application is blocked. Obtaining these data is of great help for a forensic analyst, since the analysis of these data can be vital to clarify the events that occurred in some type of criminal act. Both tools are open source under the GNU/GPLv3 license to promote their use and extensibility to applications of other instant messaging services.
Martín-Pérez, Miguel; Rodríguez, Ricardo J
Quantifying Paging on Recoverable Data from Windows User-Space Modules Proceedings Article
In: Proceedings of the 12th EAI International Conference on Digital Forensics & Cyber Crime, Springer, 2021, (Accepted for publication. To appear).
Abstract | Links | BibTeX | Tags: digital forensics, malware, memory forensics, paging, Windows modules
@inproceedings{MR-ICDF2C-21,
title = {Quantifying Paging on Recoverable Data from Windows User-Space Modules},
author = {Miguel Martín-Pérez and Ricardo J Rodríguez},
url = {https://webdiis.unizar.es/~ricardo/files/papers/MR-ICDF2C-21.pdf},
year = {2021},
date = {2021-01-01},
booktitle = {Proceedings of the 12th EAI International Conference on Digital Forensics & Cyber Crime},
publisher = {Springer},
abstract = {Memory forensic analysis enables a forensic examiner to retrieve evidence of a security incident, such as encryption keys, or analyze malware that resides solely in memory. During this process, the current state of system memory is acquired and saved to a file denoted as memory dump, which is then analyzed with dedicated software for evidence. Although a memory dump contains large amounts of data for analysis, its content can be inaccurate and incomplete due to how an operating system's memory management subsystem works: page swapping, on-demand paging, or page smearing are some of the problems that can affect the data that resides in memory. In this paper, we evaluate how these issues affect user-mode modules by measuring the ratio of modules that reside in memory on a Windows 10 system under different memory workloads. On Windows, a module represents an image (that is, an executable, shared dynamic library, or driver) that was loaded as part of the kernel or a user-mode process. We show that this ratio is particularly low in shared dynamic library modules, as opposed to executable modules. We also discuss the issues of memory forensics that can affect scanning for malicious evidences in particular. Additionally, we have developed a Volatility plugin, dubbed pluginName, which helps forensic analysts obtain paging information from a memory dump for each process running at the time of acquisition, providing them with information on the amount of data that cannot be properly analyzed.},
note = {Accepted for publication. To appear},
keywords = {digital forensics, malware, memory forensics, paging, Windows modules},
pubstate = {published},
tppubtype = {inproceedings}
}
Memory forensic analysis enables a forensic examiner to retrieve evidence of a security incident, such as encryption keys, or analyze malware that resides solely in memory. During this process, the current state of system memory is acquired and saved to a file denoted as memory dump, which is then analyzed with dedicated software for evidence. Although a memory dump contains large amounts of data for analysis, its content can be inaccurate and incomplete due to how an operating system's memory management subsystem works: page swapping, on-demand paging, or page smearing are some of the problems that can affect the data that resides in memory. In this paper, we evaluate how these issues affect user-mode modules by measuring the ratio of modules that reside in memory on a Windows 10 system under different memory workloads. On Windows, a module represents an image (that is, an executable, shared dynamic library, or driver) that was loaded as part of the kernel or a user-mode process. We show that this ratio is particularly low in shared dynamic library modules, as opposed to executable modules. We also discuss the issues of memory forensics that can affect scanning for malicious evidences in particular. Additionally, we have developed a Volatility plugin, dubbed pluginName, which helps forensic analysts obtain paging information from a memory dump for each process running at the time of acquisition, providing them with information on the amount of data that cannot be properly analyzed.