A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection

Kouliaridis, Vasileios; Kambourakis, Georgios

doi:10.3390/info12050185

Cited by 73 publications

(36 citation statements)

References 32 publications

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“…Kouliaridis and Kambourakis [144] provided a comprehensive survey on Android malware detection using machine learning techniques. e authors gave a detailed overview of the research work done over the past seven years in malware detection using machine learning.…”

Section: Android Malware Classification and Identification Usingmentioning

confidence: 99%

“…Further, they combined the system functions to depict the application behaviors and created eigenvectors. Finally, using these eigenvectors, they compared the techniques of Naive Bayesian, J48 decision tree, and application function decision algorithm for efficient identification of malware apps.e proposed tool gives better detection results when compared with the related work of other similar studies, but it also generates sufficient number of false positives.Kouliaridis and Kambourakis[144] provided a comprehensive survey on Android malware detection using machine learning techniques. e authors gave a detailed overview of the research work done over the past seven years in malware detection using machine learning.…”

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confidence: 99%

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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: Android Malware Classification and Identification Usingmentioning

confidence: 99%

mentioning

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 feature extraction methods available in the static analysis consist of two types: Manifest Analysis and Code Analysis [65]. Features such as package name, permissions, intents, activities, services, and providers can be identified in Manifest Analysis.…”

Section: Static Dynamic and Hybrid Analysismentioning

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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“…Over recent years, most studies on static feature extraction focused on extracting features, such as bytecode, sound, images, log records, code execution paths, control flow graphs, and data flow graphs. Such studies also focused on using machine learning algorithms, such as decision trees, logistic regression, Naive Bayes, support vector machines (SVM), and random forests (RF), or deep neural network (DNN)-based models, such as convolutional neural networks, generative adversarial networks, and recurrent neural networks, to detect malicious code [6]. On the other hand, current research on source code machine translation is extensive and accurate [7].…”

Section: Introductionmentioning

confidence: 99%

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

Huang¹,

Li²,

Qiao³

et al. 2022

Electronics

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Currently, among the millions of Android applications, there exist numerous malicious programs that pose significant threats to people’s security and privacy. Therefore, it is imperative to develop approaches for detecting Android malware. Recently developed malware detection methods usually rely on various features, such as application programming interface (API) sequences, images, and permissions, thereby ignoring the importance of source code and the associated comments, which are not usually included in malware. Therefore, we propose Android-SEM, which is an Android source code semantic enhancement model based on transfer learning. Our proposed model is built upon the Transformer architecture to achieve a pretraining framework for generating code comments from malware source code. The performance of the pretraining framework is optimized using a generative adversarial network. Our proposed model relies on a novel regression model-based filter to retain high-quality comments and source code for feature fusion pertinent to semantic enhancement. Creatively, and contrary to conventional methods, we incorporated a quantum support vector machine (QSVM) for classifying malicious Android code by combining quantum machine learning and classical deep learning models. The results proved that Android-SEM achieves accuracy levels of 99.55% and 99.01% for malware detection and malware categorization, respectively.

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A Comprehensive Survey on Machine Learning Techniques for Android Malware Detection

Cited by 73 publications

References 32 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

Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

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