Evaluating Machine Learning Models for Android Malware Detection

Rana, Md. Shohel; Gudla, Charan; Sung, Andrew H.

doi:10.1145/3301326.3301390

Cited by 26 publications

(8 citation statements)

References 12 publications

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“…The dataset comprises 2081 benign files and 91 malicious Android applications. Rana et al [22] applied machine learning algorithms on the Android application dataset that deals with permission access. The best accuracy is achieved by using k-Nearest Neighbours (i.e., 96%) and SVM obtained an accuracy of 93%.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems

2021

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Intelligent Transportation Systems (ITS) aim to make transportation smarter, safer, reliable, and environmentally friendly without detrimentally affecting the service quality. ITS can face security issues due to their complex, dynamic, and non-linear properties. One of the most critical security problems is attacks that damage the infrastructure of the entire ITS. Attackers can inject malware code that triggers dangerous actions such as information theft and unwanted system moves. The main objective of this study is to improve the performance of malware detection models using Graph Attention Networks. To detect malware attacks addressing ITS, a Graph Attention Network (GAN)-based framework is proposed in this study. The inputs to this framework are the Application Programming Interface (API)-call graphs obtained from malware and benign Android apk files. During the graph creation, network metrics and the Node2Vec model are utilized to generate the node features. A GAN-based model is combined with different types of node features during the experiments and the performance is compared against Graph Convolutional Network (GCN). Experimental results demonstrated that the integration of the GAN and Node2Vec models provides the best performance in terms of F-measure and accuracy parameters and, also, the use of an attention mechanism in GAN improves the performance. Furthermore, node features generated with Node2Vec resulted in a 3% increase in classification accuracy compared to the features generated with network metrics.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems

2021

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“…In Ref. 10, the authors presented a review on Android malware detection by optimizing and evaluating di®erent types of machine learning algorithms based on static analysis to detect malware in applications running on the Android OS.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

Rana

Sung

2020

Vietnam J. Comp. Sci.

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Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.

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“…In order to detect malware, Shohel Rana, Gudla, & Sung in [39] improved and analyzed certain algorithms through the implementation of a static analysis that is classifier based. When random forest was used on the dataset, 94.33 percent was obtained as the accuracy.…”

Section: Review Of Past Workmentioning

confidence: 99%

An Evaluation of some Machine Learning Algorithms for the detection of Android Applications Malware

Ezekiel¹,

Oluwasola²,

Martins³

2020

Adv. sci. technol. eng. syst. j.

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Evaluating Machine Learning Models for Android Malware Detection

Cited by 26 publications

References 12 publications

Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems

Malware Detection Based on Graph Attention Networks for Intelligent Transportation Systems

Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection

An Evaluation of some Machine Learning Algorithms for the detection of Android Applications Malware

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