Static Based Classification of Malicious Software Using Machine Learning Methods

Kutlay, Ali; Karađuzović-Hadžiabdić, Kanita

doi:10.1007/978-3-030-24986-1_49

Cited by 3 publications

(1 citation statement)

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“…However, decompiling the APK file of the software package to extract features for classification requires a long work cycle, which affects the classification efficiency. Kutlay and Karaduzovic-Hadziabdic [5] and Ahmad et al [6] both used static analysis to extract features and construct classifiers for classification through machine-learning algorithms. However, these methods usually use an unsupervised learning clustering algorithm to construct classifiers, dividing the static features into several clusters, not determining the specific category label in the Android App store.…”

Section: Related Workmentioning

confidence: 99%

Permission Sensitivity-Based Malicious Application Detection for Android

Song

Geng

Wang

et al. 2021

Security and Communication Networks

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Since a growing number of malicious applications attempt to steal users’ private data by illegally invoking permissions, application stores have carried out many malware detection methods based on application permissions. However, most of them ignore specific permission combinations and application categories that affect the detection accuracy. The features they extracted are neither representative enough to distinguish benign and malicious applications. For these problems, an Android malware detection method based on permission sensitivity is proposed. First, for each kind of application categories, the permission features and permission combination features are extracted. The sensitive permission feature set corresponding to each category label is then obtained by the feature selection method based on permission sensitivity. In the following step, the permission call situation of the application to be detected is compared with the sensitive permission feature set, and the weight allocation method is used to quantify this information into numerical features. In the proposed method of malicious application detection, three machine-learning algorithms are selected to construct the classifier model and optimize the parameters. Compared with traditional methods, the proposed method consumed 60.94% less time while still achieving high accuracy of up to 92.17%.

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Section: Related Workmentioning

confidence: 99%