Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Senanayake, Janaka; Kalutarage, Harsha; Al-Kadri, M. Omar

doi:10.3390/electronics10131606

Cited by 64 publications

(31 citation statements)

References 104 publications

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“…There is extensive literature regarding the Machine-Learning-based detection of mobile malware, and [9,10], provide recent surveys on the topic. Here, we provide an overview of related works in Android botnet detection as well as image-based detection of malicious applications.…”

Section: Related Workmentioning

confidence: 99%

A Novel Android Botnet Detection System Using Image-Based and Manifest File Features

Yerima

Bashar

2022

Electronics

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Malicious botnet applications have become a serious threat and are increasingly incorporating sophisticated detection avoidance techniques. Hence, there is a need for more effective mitigation approaches to combat the rise of Android botnets. Although the use of Machine Learning to detect botnets has been a focus of recent research efforts, several challenges remain. To overcome the limitations of using hand-crafted features for Machine-Learning-based detection, in this paper, we propose a novel mobile botnet detection system based on features extracted from images and a manifest file. The scheme employs a Histogram of Oriented Gradients and byte histograms obtained from images representing the app executable and combines these with features derived from the manifest files. Feature selection is then applied to utilize the best features for classification with Machine-Learning algorithms. The proposed system was evaluated using the ISCX botnet dataset, and the experimental results demonstrate its effectiveness with F1 scores ranging from 0.923 to 0.96 using popular Machine-Learning algorithms. Furthermore, with the Extra Trees model, up to 97.5% overall accuracy was obtained using an 80:20 train–test split, and 96% overall accuracy was obtained using 10-fold cross validation.

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Section: Related Workmentioning

confidence: 99%

A Novel Android Botnet Detection System Using Image-Based and Manifest File Features

Yerima

Bashar

2022

Electronics

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“…The reviewed papers were compared and analysed according to the input features, the classification algorithm, and the characteristics of the dataset. Another systematic review of machine-learning-based Android malware detection techniques was presented by Senanayake et al [13]. The authors of this article critically evaluated 106 articles, highlighting their strengths and weaknesses as well as potential improvements.…”

Section: Detection Using Machine Learningmentioning

confidence: 99%

MLMD—A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm

et al. 2022

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This paper focuses on training machine learning models using the XGBoost and extremely randomized trees algorithms on two datasets obtained using static and dynamic analysis of real malicious and benign samples. We then compare their success rates—both mutually and with other algorithms, such as the random forest, the decision tree, the support vector machine, and the naïve Bayes algorithms, which we compared in our previous work on the same datasets. The best performing classification models, using the XGBoost algorithm, achieved 91.9% detection accuracy and 98.2% sensitivity, 0.853 AUC, and 0.949 F1 score on the static analysis dataset, and 96.4% accuracy and 98.5% sensitivity, 0.940 AUC, and 0.977 F1 score on the dynamic analysis dataset. Then, we exported the best performing machine learning models and used them in our proposed MLMD program, automating the process of static and dynamic analysis and allowing the trained models to be used for classification on new samples.

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“…Previous systematic reviews have discussed mobile malware detection technology and methods to improve mobile security [156][157][158][159][160][161][162]. Feizollah et al [156] reviewed 100 papers from 2010 to 2014, concentrating on the features of mobile malware detection.…”

Section: Mobile Malware Detectionmentioning

confidence: 99%

“…However, this review did not explain systematic research collection procedures. Senanayake et al [157] conducted a systematic literature review. However, they analyzed papers by classifying them into static, dynamic, and hybrid analysis functions.…”

Section: Mobile Malware Detectionmentioning

confidence: 99%

A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection

Kim

Lee

et al. 2022

Security and Communication Networks

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With the deployment of the 5G cellular system, the upsurge of diverse mobile applications and devices has increased the potential challenges and threats posed to users. Industry and academia have attempted to address cyber security challenges by implementing automated malware detection and machine learning algorithms. This study expands on previous research on machine learning-based mobile malware detection. We critically evaluate 154 selected articles and highlight their strengths and weaknesses as well as potential improvements. We explore the mobile malware detection techniques used in recent studies based on attack intentions, such as server, network, client software, client hardware, and user. In contrast to other SLR studies, our study classified the means of attack as supervised and unsupervised learning. Therefore, this article aims at providing researchers with in-depth knowledge in the field and identifying potential future research and a framework for a thorough evaluation. Furthermore, we review and summarize security challenges related to cybersecurity that can lead to more effective and practical research.

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Android Mobile Malware Detection Using Machine Learning: A Systematic Review

Cited by 64 publications

References 104 publications

A Novel Android Botnet Detection System Using Image-Based and Manifest File Features

A Novel Android Botnet Detection System Using Image-Based and Manifest File Features

MLMD—A Malware-Detecting Antivirus Tool Based on the XGBoost Machine Learning Algorithm

A Systematic Overview of the Machine Learning Methods for Mobile Malware Detection

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