RETRACTED ARTICLE: A novel permission ranking system for android malware detection—the permission grader

Dharmalingam, Varna Priya; Visalakshi, P.

doi:10.1007/s12652-020-01957-5

Cited by 23 publications

(4 citation statements)

References 19 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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“…One distinguishing aspect of this study is the inclusion of additional details, such as application name and version, during the malware detection process. Dharmalingam and Palanisamy (2021) proposed the Permission Grading System (PGS) to extract requested permissions from applications and detect the permissions that are special to malware and benign apps, with the contribution of each permission calculated. The presented model is effective only when malware and benign applications have different sets of permission.…”

Section: Manifest Featuresmentioning

confidence: 99%

Guarding Android: A Comprehensive Review of Intrusion Detection Techniques for Smartphones

Ibrahim,

Sallow

2023

SJUOZ

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The popularity of using the Android operating system has increased the number of developers and intruders in this field. Many applications are developed in this area and perform malicious activities like ransomware attacks, installing backdoors, phishing, sending premium short message service, and stealing private data. These activities pose many threats to smartphone users. This study provides a review of the main strategies used in intrusion detection systems to detect malicious activities at the application and system levels. The study illustrates the advantages and disadvantages of each method and the significant features used to discriminate against malicious activities and highlights several open issues that warrant further investigation and improvement. It is a comprehensive review that may be useful for academic researchers interested in cybersecurity.

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“…A importância das características para o processo de detecc ¸ão de malwares é facilmente compreendida quando se empregam soluc ¸ões baseadas no aprendizado de máquina [Wang et al, 2019, Sharma andRattan, 2021]. Isto porque, para o correto funcionamento, essas soluc ¸ões (e seus modelos) dependem de conjuntos de dados (datasets) corretos e atualizados, requerendo assim a extrac ¸ão detalhada de características dos aplicativos [Dharmalingam and Palanisamy, 2021, Alazab et al, 2020, Zhang et al, 2020.…”

Section: Introduc ¸ãOunclassified

Ferramentas de extração de características para análise estática de aplicativos Android

Pontes¹,

Costa²,

Rocha³

et al. 2021

Anais Da XIX Escola Regional De Redes De Computadores (ERRC 2021)

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Neste estudo, investigamos algumas das ferramentas utilizadas para a extração de características estáticas de aplicações Android, que são frequentemente utilizadas por métodos de detecção de malwares. Demonstramos que as ferramentas podem diferir quanto ao quantitativo e a apresentação dos dados extraídos, o que acaba condicionando sua aplicabilidade às necessidades específicas de cada projeto.

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RETRACTED ARTICLE: A novel permission ranking system for android malware detection—the permission grader

Cited by 23 publications

References 19 publications

The rise of obfuscated Android malware and impacts on detection methods

The rise of obfuscated Android malware and impacts on detection methods

Guarding Android: A Comprehensive Review of Intrusion Detection Techniques for Smartphones

Ferramentas de extração de características para análise estática de aplicativos Android

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