A Survey of Android Malware Detection Based on Deep Learning

Wang, Dianxin; Chen, Tian; Zhang, Zheng; Zhang, Nan

doi:10.1007/978-3-031-20096-0_18

Cited by 7 publications

(4 citation statements)

References 62 publications

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“…Injection game cheating programs often have a high degree of specificity, specifically targeting at one game to implement cheating functions. The implementation of injection game cheating programs requires the use of plugins to inject functional modules into the game process space (Wang et al, 2023). After being injected into the game process space, the functional module can perform a HOOK operation, and through the HOOK operation, it can jump to the code written by the game cheating program author to perform the program written by the game cheating program author.…”

Section: Injection Game Cheating Programmentioning

confidence: 99%

Analyzing the “Intrusiveness” and “Destructiveness” of Different Types of Game Cheating Programs

2024

IJLPS

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According to China’s criminal law, the production and sale of online game cheating programs are often charged of “providing special programs specially used for intruding into computer information system”. And they are also sometimes charged of “deliberately creates or propagates computer virus and other destructive programs”. Some types of game cheating programs such as “injection game cheating program” meet the criterion of “intruding into computer information system” in the Criminal Law, while other types of game cheating programs such as “offline game cheating program” do not. The “Destructiveness” of game cheating programs should be analyzed according to specific game cheating program and the legal interests it embodied in computer information systems. It should be determined on the basis of legislator’s intention, rather than directly use the criterion of evaluating the “destructiveness” of programs in computer science.

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Section: Injection Game Cheating Programmentioning

confidence: 99%

Analyzing the “Intrusiveness” and “Destructiveness” of Different Types of Game Cheating Programs

2024

IJLPS

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“…The Androtomist tool proposed in [34] [62], and Wu et al [10], as well as the many references therein.…”

Section: Other Approaches and Surveys On The Topicmentioning

confidence: 99%

Explainable Machine Learning for Malware Detection on Android Applications

Palma,

Ferreira,

Figueiredo

2024

Information

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The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device. Despite the protections app stores provide to avoid malware, it keeps growing in sophistication and diffusion. In this paper, we explore the use of machine learning (ML) techniques to detect malware in Android apps. The focus is on the study of different data pre-processing, dimensionality reduction, and classification techniques, assessing the generalization ability of the learned models using public domain datasets and specifically developed apps. We find that the classifiers that achieve better performance for this task are support vector machines (SVM) and random forests (RF). We emphasize the use of feature selection (FS) techniques to reduce the data dimensionality and to identify the most relevant features in Android malware classification, leading to explainability on this task. Our approach can identify the most relevant features to classify an app as malware. Namely, we conclude that permissions play a prominent role in Android malware detection. The proposed approach reduces the data dimensionality while achieving high accuracy in identifying malware in Android apps.

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“…Wang et al [20] conducted a survey of Android malware detection tools and techniques and provided an overview of commonly used algorithms, datasets, evaluation metrics, and indicators. The goal of this article is to provide a guide to Android malware detection and antivirus solutions that can cater to malware spread.…”

Section: Literature Reviewmentioning

confidence: 99%

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

et al. 2023

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There is an exponential rise in the use of smartphones in government and private institutions due to business dependencies such as communication, virtual meetings, and access to global information. These smartphones are an attractive target for cybercriminals and are one of the leading causes of cyber espionage and sabotage. A large number of sophisticated malware attacks as well as advanced persistent threats (APTs) have been launched on smartphone users. These attacks are becoming significantly more complex, sophisticated, persistent, and undetected for extended periods. Traditionally, devices are targeted by exploiting a vulnerability in the operating system (OS) or device sensors. Nevertheless, there is a rise in APTs, side-channel attacks, sensor-based attacks, and attacks launched through the Google Play Store. Previous research contributions have lacked contemporary threats, and some have proven ineffective against the latest variants of the mobile operating system. In this paper, we conducted an extensive survey of papers over the last 15 years (2009–2023), covering vulnerabilities, contemporary threats, and corresponding defenses. The research highlights APTs, classifies malware variants, defines how sensors are exploited, visualizes multiple ways that side-channel attacks are launched, and provides a comprehensive list of malware families that spread through the Google Play Store. In addition, the research provides details on threat defense solutions, such as malware detection tools and techniques presented in the last decade. Finally, it highlights open issues and identifies the research gap that needs to be addressed to meet the challenges of next-generation smartphones.

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A Survey of Android Malware Detection Based on Deep Learning

Cited by 7 publications

References 62 publications

Analyzing the “Intrusiveness” and “Destructiveness” of Different Types of Game Cheating Programs

Analyzing the “Intrusiveness” and “Destructiveness” of Different Types of Game Cheating Programs

Explainable Machine Learning for Malware Detection on Android Applications

Smartphone Security and Privacy: A Survey on APTs, Sensor-Based Attacks, Side-Channel Attacks, Google Play Attacks, and Defenses

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