Detection of anomalous behavior of smartphones using signal processing and machine learning techniques

James, R. Soundar Raja; Albasir, Abdurhman; Naik, Kshirasagar; Dabbagh, Mohamed-Yahia; Dash, Prajna Paramita; Zamani, Mohsen; Goel, Nishith

doi:10.1109/pimrc.2017.8292418

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Cited by 4 publications

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“…The model achieved accuracy of 76.2%, 88.5%, and 87.2% for groups one, two and three, respectively. James et al [92] detected Android malware by employing signal processing and statistical learning. The authors initially performed ground truth determination using Fast Fourier transform, a low-pass Butterworth filter, and inverse fast Fourier transform techniques.…”

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confidence: 99%

Recent Advances in Android Mobile Malware Detection: A Systematic Literature Review

Alzubaidi¹

2021

IEEE Access

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In recent years, the global pervasiveness of smartphones has prompted the development of millions of free and commercially available applications. These applications allow users to perform various activities, such as communicating, gaming, and completing financial and educational tasks. These commonly used devices often store sensitive private information and, consequently, have been increasingly targeted by harmful malicious software. This paper focuses on the concepts and risks associated with malware, and reviews current approaches and mechanisms used to detect malware with respect to their methodology, associated datasets, and evaluation metrics.

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Section: Tip Calculatormentioning

confidence: 99%