Android malware detection based on sensitive patterns

Liu, Kang; Zhang, Guanghui; Chen, Xue; Liu, Qing; PENG, Linyu; Yurui, Liu

doi:10.1007/s11235-022-00983-2

Cited by 7 publications

(4 citation statements)

References 40 publications

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“…In SeGDroid [18], a Graph Convolutional Neural Network (GCN) classifier is used in the sensitive function call graph corresponding to an application for malware detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Android Malware Detection Based on Informative Syscall Subsequences

Surendran,

Uddin,

Thomas

et al. 2024

IEEE Access

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The Android operating system commands a dominant market share of over 70% in the smartphone industry. However, this widespread usage has resulted in a concerning increase in malware applications. While existing static malware detection mechanisms are vulnerable to code obfuscation attacks, manipulating the runtime system call (syscall) sequence remains a significant challenge for attackers. Consequently, syscall-based malware detection mechanisms are gaining prominence. Current syscall-based malware detection approaches rely on machine learning algorithms, utilizing numerical features such as syscall frequencies and transition probability matrices. However, the wide range of values in these features necessitates large datasets for effective classifier training, and susceptibility to noise and outliers persists. As a result, there is an urgent need for a binary representation of dynamic features to improve malware detection efficiency. To address this challenge, our paper proposes an innovative syscall subsequence-based binary feature representation method for machine learning-driven malware detection. By employing the information gain method, we identify informative syscall subsequences. The proposed mechanism achieves an impressive 99% accuracy in detecting malware applications using just 50% of the training data, across both the Drebin/AMD and CICMalDroid2020 datasets.

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“…In SeGDroid [18], a Graph Convolutional Neural Network (GCN) classifier is used in the sensitive function call graph corresponding to an application for malware detection.…”

Section: Literature Reviewmentioning

confidence: 99%

Android Malware Detection Based on Informative Syscall Subsequences

Surendran,

Uddin,

Thomas

et al. 2024

IEEE Access

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“…MH-100K dataset [6] CICAndMal2017 [7], [4] CICInvesAndMal 2019 [8] CCCS-CIC-AndMal-2020 [9] Andro-AutoPsy [10] Android Malware, McAfee Labs [11] AndroZoo [12] To further confirm the efficacy of the suggested strategy, the authors advise running trials on a bigger dataset. To give users access to real-time risk assessment, future research can also investigate integrating the risk detection system into app stores or security tools.…”

Section: Dataset Names Behavioral Features Machine Learning Deep Lear...mentioning

confidence: 99%

“…Android operating system (OS) occupies most of the market share. According to the global smartphone operating system market report released by International Data Corporation (IDC), Android leads the way with a market share of 86.7% [4], [5] Its open-source design and ease of customization at many levels drew in users. They encouraged manufacturers to focus on creating affordable smart devices.…”

Section: Introductionmentioning

confidence: 99%

Mobile based Malware Detection using Artificial Intelligence Techniques a review

Faez mahdi,

Saadoon Jasim

2024

J. Al-Qadisiyah Comp. Sci. Math.

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Malware attacks on mobile devices are becoming more common and more complicated every year. Malware writers see the open-source Android app as their main target because so many people use it. Artificial intelligence is used by most of the literature's mobile malware detection methods to find ransomware. Our research, on the other hand, makes it clear that most of the earlier studies used different metrics and models, as well as different datasets and classification features that came from static, dynamic, or hybrid analysis strategies. This makes comparing the different suggested detection methods more difficult and may also make the results less certain. The goal of this work is to solve the problem of AI-powered malware detection by sorting current methods and approaches into three groups: the type of dataset, the type of detection method used (machine learning models, deep learning models, and Behavioral Analysis model), and how well the method works. In this way, we suggest a convergent plan that can be used as a basis for future methods of finding malware on Android and as a solid standard for artificial intelligence work in this area.

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“…McLauglin [68] McAfee, vendor's internal dataset Wang et al [69] Mal com1, Mal com2 and Mal Zhou [220] Grace et al [70] Github Liu et al [71] VirusShare Bayazit et al [72] CICInvesAndMal2019 Lee et al [73] Andro-AutoPsy Dataset [221] Zhu et al [74] MUDFLOW [222], VirusShare Almahmoud et al [75] CIC-AndMal2017, CIC-InvesAndMal2019, CIC-MalDroid2020 Feng et al [76] CICAndMal2017 Kandu et al [77] Genome Arora et al [78] Genome Ding et al [79] CICInvesAndMal2019 Sahin et al [80] M0Droid [223], AMD, Kaggle, [224] Idrees et al [81] Contagio, Drebin, Genome, Virus Total, theZoo, MalShare, VirusShare Khariwal et al [82] Genome, Drebin, Koodous Idrees et al [83] Contagio, VirusTotal, appsapk, Androidmob Zhu et al [15] VirusShare Bai et al [84] Drebin Taheri et al [85] Drebin, Contagio, Genome Alazab et al [86] AndroZoo, Contagio, MalShare, VirusShare, VirusTotal Mathur et al [87] Androzoo, AMD Imtiaz et al [88] CICInves AndMal2019 Liu et al [89] OmniDroid, CIC2019, CIC2020 Chen et al [90] VirusShare Guan et al [91] VirusShare Mohamed et al [92] Genome, Maldroid Varma et al [93] CICInvesAnd Mal2019 Gyunka et al [94] Genome, Contagio Taha et al [95] Drebin Peng et al [96] CICMalDroid 2020, CIC-InvesAndMal 2019, Drebin Ashwini et al [97] Drebin Jiang et al [98] Genome, Andro MalShare Wang et al [99] Information Security Lab of Peking University Rana et al …”

Section: Related Workmentioning

confidence: 99%

A comprehensive review on permissions-based Android malware detection

Sharma,

Arora

2024

Int. J. Inf. Secur.

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The first Android-ready "G1" phone debuted in late October 2008. Since then, the growth of Android malware has been explosive analogous to the rise in the popularity of Android. The major positive aspect of Android has been its open-source nature, which empowers app developers to expand their work. But at the same time, authors with malicious intentions pose grave threats to users. In the presence of such threats, Android malware detection is the need of an hour. Consequently, researchers have proposed various techniques involving static, dynamic, and hybrid analysis to address such threats using numerous features in the last decade. But the feature that most researchers have extensively used to perform malware analysis and detection in Android security is Android permission. Hence, to provide a clarified overview of the latest and past work done in Android malware analysis and detection, we perform a comprehensive literature review using permissions as a central feature or in combination with other components by collecting and analyzing 200 studies from January 2009 to February 2023. We extracted information such as the choice opted by researchers between analysis or detection, techniques used to select or rank the permissions feature set, features used along with permissions, detection models employed, the malware datasets used by researchers, and lastly, the limitations and challenges in the field of Android malware detection to propose some future research directions. Additionally, based on the information extracted, we answer the six research questions designed considering the above factors.

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Android malware detection based on sensitive patterns

Cited by 7 publications

References 40 publications

Android Malware Detection Based on Informative Syscall Subsequences

Android Malware Detection Based on Informative Syscall Subsequences

Mobile based Malware Detection using Artificial Intelligence Techniques a review

A comprehensive review on permissions-based Android malware detection

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