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Erschienen in:
Experimental Analysis on Big Data in IOT-Based Architecture Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

NeSeDroid—Android Malware Detection Based on Network Traffic and Sensitive Resource Accessing

verfasst von : Nguyen Tan Cam, Nguyen Cam Hong Phuoc

Erschienen in: Proceedings of the International Conference on Data Engineering and Communication Technology

Verlag: Springer Singapore

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Abstract

The Android operating system has a large market share. The number of new malware on Android is increasing much recently. Android malware analysis includes static analysis and dynamic analysis. Limitations of static analysis are the difficulty in analyzing the malware using encryption techniques, to confuse the source, and to change behavior itself. In this paper, we proposed a hybrid analysis method, named NeSeDroid. This method used static analysis to detect the sensitive resource accessing. It also used dynamic analysis to detect sensitive resource leakage, through Internet connection. The method is tested on the list of applications which are downloaded from Android Apps Market, Genome Malware Project dataset and our additional samples in DroidBench dataset. The evaluation results show that the NeSeDroid has the high accuracy and it reduces the rate of fail positive detection.

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Vorheriges Kapitel Morphology Based Approach for Number Plate Extraction
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Metadaten
Titel
NeSeDroid—Android Malware Detection Based on Network Traffic and Sensitive Resource Accessing
verfasst von
Nguyen Tan Cam
Nguyen Cam Hong Phuoc
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Proceedings of the International Conference on Data Engineering and Communication Technology

Print ISBN: 978-981-10-1677-6

Electronic ISBN: 978-981-10-1678-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-1678-3_3