Recent Advances in Android Mobile Malware Detection: A Systematic Literature Review

Alzubaidi, Abdulaziz

doi:10.1109/access.2021.3123187

Cited by 13 publications

(6 citation statements)

References 174 publications

(228 reference statements)

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“…Machine learning-based approaches can efficiently detect Android malware, but they require detailed knowledge of feature classification and feature extraction. Deep Learning approaches automatically extract useful features, but they require high computational power to train the dataset [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Android Security: Threats, Vulnerabilities, Malware Detection, and Analysis

Acharya

Rawat

Bhatnagar

2022

Security and Communication Networks

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The popularity and open-source nature of Android devices have resulted in a dramatic growth of Android malware. Malware developers are also able to evade the detection methods, reducing the efficiency of malware detection techniques. It is hence desirable that security researchers and experts come up with novel and more efficient methods to analyze existing and zero-day Android malware. Most of the researchers have focused on Android system security. However, to examine Android security, with a specific focus on malware development, investigation of malware prevention techniques and already known malware detection techniques needs a broad inclusion. To overcome the research gaps, this paper provides a broad review of current Android security concerns, security implementation enhancements, significant malware detected during 2017–2021, and stealth procedures used by the malware developers along with the current Android malware detection techniques. A comparative analysis is presented between this article and similar recent survey articles to fill the existing research gaps. In the end, a three-phase model is proposed to efficiently identify and characterize Android malware. In the first phase, a lightweight deep transfer learning approach is used to classify Android applications into benign and malicious. In the second phase, the malicious applications are executed in a virtual emulator to reduce the number of false positives. Finally, the malicious applications having the same characteristic ratio are grouped into their corresponding families using the topic modelling approach. The proposed model can efficiently detect, characterize, and provide a familial classification of Android malware with a good accuracy rate.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Android Security: Threats, Vulnerabilities, Malware Detection, and Analysis

Acharya

Rawat

Bhatnagar

2022

Security and Communication Networks

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“…Kouliaridis et al (2020) compare 22 mobile malware detection approaches, encompassing works published between 2009 and 2018. Alzubaidi (2021) provides a paper highlighting the ideas and dangers associated with malware and carefully analyses the methods and procedures currently used for malware detection, considering their methodology, relevant data sets, and evaluation metrics. Kouliaridis and Kambourakis (2021) focus on ML‐powered Android malware detection techniques based on various aspects such as feature extraction method, ML classification techniques, analysis type, performance evaluation metrics, and data set.…”

Section: Related Workmentioning

confidence: 99%

“…Consequently, the effectiveness of the employed classifier is inherently tied to the quality of this preparatory phase (Bakour et al, 2019). Authors are advised to incorporate the preprocessing stage as an integral step to mitigate potential errors that might have arisen during the data collection procedures (Alzubaidi, 2021). Collecting and analysing data from different sources is referred to as data collection.…”

Section: Android Malware Detection Modelmentioning

confidence: 99%

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A review of detecting malware in android devices based on machine learning techniques

Sharma,

Kaul

2023

Expert Systems

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Malware developers install malware on mobile users' devices and steal their personal information without their knowledge. According to recent studies, it has been observed that malware developers are now targeting Android mobile devices. Researchers have examined the issues of detecting malware in these devices and proposed different methods and techniques. This study's main goal is to aid researchers in gaining a basic understanding of Android malware and its numerous detection methods. Earlier experiments that used machine learning to detect Android malware will be carefully reviewed in this paper. This in‐depth review article thoroughly examines the origins, evolution, and sustainability of Android malware detection. It offers an in‐depth literature review that includes the most recent approaches and research trends for detecting malware, from static analysis to dynamic analysis, machine learning, and deep learning. Additionally, we review current approaches' shortcomings and difficulties and suggest possible paths for further investigation. The paper aims to stimulate further innovation in this essential field by providing researchers and practitioners with a comprehensive overview of the current status of Android malware detection.

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“…As the only widely utilized OS globally, Android is exceedingly focused on by malevolent programmers. Much work has been done to differentiate Android malware, but programmers are making progress to circumvent malware classifiers [10,11]. Since it is open-source-based, Android devices are primarily targeted by attackers.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Anubis Trojan Attack on Android Banking Application Using Mobile Security Labware

Riadi¹,

Sunardi²,

Aprilliansyah³

2023

IJSSE

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Mobile banking transactions, especially on the Android platform, are now a method often used to carry out payment and shopping activities in e-commerce. Security risks in mobile banking and online banking on the Android platform have security and privacy issues that must be maintained. Kaspersky conducted research on banking Trojans and found 97,661 variants of banking Trojans and 17,372 new variants. Based on this, this investigation was conducted by using mobile security labware to simulate and analyze the Anubis Trojan malware on Android devices. All activities are caused by the Anubis Trojan, which forces users to activate an access service that can read all users' activities and run them in the background. This research can help investigate the types of trojan malware on the Android operating system.

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Recent Advances in Android Mobile Malware Detection: A Systematic Literature Review

Cited by 13 publications

References 174 publications

A Comprehensive Review of Android Security: Threats, Vulnerabilities, Malware Detection, and Analysis

A Comprehensive Review of Android Security: Threats, Vulnerabilities, Malware Detection, and Analysis

A review of detecting malware in android devices based on machine learning techniques

Analysis of Anubis Trojan Attack on Android Banking Application Using Mobile Security Labware

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