A novel permission-based Android malware detection system using feature selection based on linear regression

Şahin, Durmuş Özkan; Kural, Oğuz Emre; Akleylek, Sedat; Kılıç, Erdal

doi:10.1007/s00521-021-05875-1

Cited by 33 publications

(14 citation statements)

References 34 publications

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“…Until today, many static analysis researchers depends on permissions ( Arora, Peddoju & Conti, 2019 ; Dharmalingam & Palanisamy, 2021 ; Li et al, 2018 ; Şahin et al, 2021 ); however, many are relying on API calls ( Alazab et al, 2020 ; Jung et al, 2018 ; Maiorca et al, 2017 ; Mirzaei et al, 2019 ; Pektaş & Acarman, 2020 ; Tiwari & Shukla, 2018 ; Zhang et al, 2020 ; Zhang, Breitinger & Baggili, 2016 ; Zou et al, 2021 ) and deep code analysis and other types of features as discussed earlier in Android evasion detection frameworks section. Many of examined researches ignored the evasion techniques evaluation.…”

Section: Discussionmentioning

confidence: 99%

“… Permission-based: APK Auditor ( Talha, Alper & Aydin, 2015 ) is a static model that leverages permission-based detection castoff decompressing the APK package; it extracts the malicious symptoms using permission and signature matching analysis. Likewise, Triggerscope ( Fratantonio et al, 2016 ) uses permissions characteristics as an input to classify the application using different machine learning algorithms ( Abdulla & Altaher, 2015 ; Alazab et al, 2020 ; Arora, Peddoju & Conti, 2019 ; Dharmalingam & Palanisamy, 2021 ; Fang, Han & Li, 2014 ; Glodek & Harang, 2013 ; Li et al, 2018 ; Niazi et al, 2015 ; Şahin et al, 2021 ; Shalaginov & Franke, 2014 ; Talha, Alper & Aydin, 2015 ; Tiwari & Shukla, 2018 ). Source code based Analysis: Arp et al (2015) extracts features from the application’s Androidmanifest file and source code ; it scrutinizes the code by listing the native calls , API calls , and URL addresses .…”

Section: Evasion Techniquesmentioning

confidence: 99%

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The rise of obfuscated Android malware and impacts on detection methods

Elsersy¹,

Feizollah²,

Anuar³

2022

PeerJ Computer Science

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The various application markets are facing an exponential growth of Android malware. Every day, thousands of new Android malware applications emerge. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious code. Therefore, Android applications developers tend to use state-of-the-art obfuscation techniques to mitigate the risk of application plagiarism. The malware authors adopt the obfuscation and transformation techniques to defeat the anti-malware detections, which this paper refers to as evasions. Malware authors use obfuscation techniques to generate new malware variants from the same malicious code. The concern of encountering difficulties in malware reverse engineering motivates researchers to secure the source code of benign Android applications using evasion techniques. This study reviews the state-of-the-art evasion tools and techniques. The study criticizes the existing research gap of detection in the latest Android malware detection frameworks and challenges the classification performance against various evasion techniques. The study concludes the research gaps in evaluating the current Android malware detection framework robustness against state-of-the-art evasion techniques. The study concludes the recent Android malware detection-related issues and lessons learned which require researchers’ attention in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Evasion Techniquesmentioning

confidence: 99%

The rise of obfuscated Android malware and impacts on detection methods

Elsersy¹,

Feizollah²,

Anuar³

2022

PeerJ Computer Science

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“…Şahin et al presented, in 2021, a machine-learning based malware detection system that utilizes deep neural networks [21]. The proposed system relies on features extracted from the application permissions.…”

Section: B Machine Learning-based Malware Detectionmentioning

confidence: 99%

PAIRED: An Explainable Lightweight Android Malware Detection System

Alani

Awad

2022

IEEE Access

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With approximately 2 billion active devices, the Android operating system tops all other operating systems in terms of the number of devices using it. Android has gained wide popularity not only as a smartphone operating system, but also as an operating system for vehicles, tablets, smart appliances, and Internet of Things devices. Consequently, security challenges have arisen with the rapid adoption of the Android operating system. Thousands of malicious applications have been created and are being downloaded by unsuspecting users. This paper presents a lightweight Android malware detection system based on explainable machine learning. The proposed system uses the features extracted from applications to identify malicious and benign malware. The proposed system is tested, showing an accuracy exceeding 98% while maintaining its small footprint on the device. In addition, the classifier model is explained using Shapley Additive Explanation (SHAP) values.

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“…The proposed work in [70] checked the possibility of using reduced dimension vector generating for malware detection. Based on that, malware detection using ML models with permission-based static analysis was performed.…”

Section: Manifest Based Static Analysis With MLmentioning

confidence: 99%

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

2021

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With the increasing use of mobile devices, malware attacks are rising, especially on Android phones, which account for 72.2% of the total market share. Hackers try to attack smartphones with various methods such as credential theft, surveillance, and malicious advertising. Among numerous countermeasures, machine learning (ML)-based methods have proven to be an effective means of detecting these attacks, as they are able to derive a classifier from a set of training examples, thus eliminating the need for an explicit definition of the signatures when developing malware detectors. This paper provides a systematic review of ML-based Android malware detection techniques. It critically evaluates 106 carefully selected articles and highlights their strengths and weaknesses as well as potential improvements. Finally, the ML-based methods for detecting source code vulnerabilities are discussed, because it might be more difficult to add security after the app is deployed. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in the field and to identify potential future research and development directions.

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A novel permission-based Android malware detection system using feature selection based on linear regression

Cited by 33 publications

References 34 publications

The rise of obfuscated Android malware and impacts on detection methods

The rise of obfuscated Android malware and impacts on detection methods

PAIRED: An Explainable Lightweight Android Malware Detection System

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

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