Process control block information dataset: Towards android malware detection

This article proposes a Process Control Block (PCB) dataset [1] mined over the process execution time of tested Android applications. The PCB data from 2620 malware-infested applications and 1610 benign applications were collected. The PCB data sequence was collected for 25 seconds, with an average of 18,500 PCB records stored for each application.The mining method was implemented at the kernel level and synced with the process (job) context switching. The data for each program comprises the PCB information for all threads running the application. The application automation testing and PCB gathering for benign and malicious applications were conducted in a closed dynamic malware analysis framework. The dataset can be used to compare and contrast the low-level (kernel) behavior of benign and malicious Android programs. For the vast majority of tested applications, the mining approach effectively captured 99% of the context switches.

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Publication Details

Journal
Data in Brief
Published
2024-09-26
DOI
https://doi.org/10.1016/j.dib.2024.110975
Citations
2
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.70

Funders

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article

Process control block information dataset: Towards android malware detection

Heba Alawneh, Hamza Alkofahi
2 citations
Data in Brief
Advanced Malware Detection Techniques
0.70
article

Process control block information dataset: Towards android malware detection

Heba Alawneh, Hamza Alkofahi
article en
2 citations

Abstract

This article proposes a Process Control Block (PCB) dataset [1] mined over the process execution time of tested Android applications. The PCB data from 2620 malware-infested applications and 1610 benign applications were collected. The PCB data sequence was collected for 25 seconds, with an average of 18,500 PCB records stored for each application.The mining method was implemented at the kernel level and synced with the process (job) context switching. The data for each program comprises the PCB information for all threads running the application. The application automation testing and PCB gathering for benign and malicious applications were conducted in a closed dynamic malware analysis framework. The dataset can be used to compare and contrast the low-level (kernel) behavior of benign and malicious Android programs. For the vast majority of tested applications, the mining approach effectively captured 99% of the context switches.

Data in BriefVol. 57
Jordan University of Science and Technology (JO)
Auburn University
Openalex Percentile: Top 31%
Advanced Malware Detection Techniques
0.70
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Process control block information dataset: Towards android malware detection — Heba Alawneh, Hamza Alkofahi · Data in Brief (2024) | TGRS Research Map | TGRS