Analysis of Advanced Volatile Threats Using Memory Forensics

Gadgil, Priya; Nagpure, Sangeeta

doi:10.2139/ssrn.3358798

Cited by 4 publications

(1 citation statement)

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“…This technique is especially useful for the analysis of threats known as "fileless malware" or "memory-based attacks". In the article published by Gadgil & Nagpure [24] they give an insight into how certain types of malware do not install files on the target's hard drive to execute malicious activities. Malware lives directly in memory and can take advantage of system tools to inject code into trusted and safe processes such as javaw.exe or iexplorer.exe.…”

Section: Xxxxmentioning

confidence: 99%

Benchmarking Android malware analysis tools

Higuera,

Moreno,

Higuera

et al. 2023

Preprint

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Today, malware is arguably one of the biggest challenges organizations face from a cybersecurity standpoint, regardless of the types of devices used in the organization. One of the most malware-attacked mobile operating systems today is Android. In response to this threat, this paper presents research on the functionalities and performance of different malicious Android application package analysis tools including one that uses machine learning techniques. In addition, it investigates how the use of these tools streamlines the process of detection, classification, and analysis of malicious APKs for Android operating system devices. The tools, that use Artificial Intelligence techniques, are more efficient than other current tools that do not use them. In this way, new approaches can be suggested in the specification, design, and development of new tools that help to analyze, from a cybersecurity point of view, the code of applications developed for this environment.

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Section: Xxxxmentioning

confidence: 99%

Benchmarking Android malware analysis tools

Higuera,

Moreno,

Higuera

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

A Systematic Literature Review on Volatility Memory Forensics

Hamid¹,

Alabdulhay²,

Rahman³

2023

Advances in Intelligent Systems and Computing

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Benchmarking Android Malware Analysis Tools

Bermejo Higuera,

Morales Moreno,

Bermejo Higuera

et al. 2024

Electronics

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Today, malware is arguably one of the biggest challenges organisations face from a cybersecurity standpoint, regardless of the types of devices used in the organisation. One of the most malware-attacked mobile operating systems today is Android. In response to this threat, this paper presents research on the functionalities and performance of different malicious Android application package analysis tools, including one that uses machine learning techniques. In addition, it investigates how these tools streamline the detection, classification, and analysis of malicious Android Application Packages (APKs) for Android operating system devices. As a result of the research included in this article, it can be highlighted that the AndroPytool, a tool that uses machine learning (ML) techniques, obtained the best results with an accuracy of 0.986, so it can be affirmed that the tools that use artificial intelligence techniques used in this study are more efficient in terms of detection capacity. On the other hand, of the online tools analysed, Virustotal and Pithus obtained the best results. Based on the above, new approaches can be suggested in the specification, design, and development of new tools that help to analyse, from a cybersecurity point of view, the code of applications developed for this environment.

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Analysis of Advanced Volatile Threats Using Memory Forensics

Cited by 4 publications

References 0 publications

Benchmarking Android malware analysis tools

Benchmarking Android malware analysis tools

A Systematic Literature Review on Volatility Memory Forensics

Benchmarking Android Malware Analysis Tools

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