An effective detection of mobile malware behavior using network traffic :TrafficAV

Vinisha Malik , Dr. Sandip Kumar Goyal

Android has become the most popular mobile plat- form due to its openness and flexibility. Meanwhile, it has also become the main target of massive mobile malware. This phenomenon drives a pressing need for malware detection. In this paper, we propose TrafficAV, which is an effective and explainable detection of mobile malware behavior using network traffic. Network traffic generated by mobile app is mirrored from the wireless access point to the server for data analysis. All data analysis and malware detection are performed on the server side, which consumes minimum resources on mobile devices without affecting the user experience. Due to the difficulty in identifying disparate malicious behaviors of malware from the network traffic, TrafficAV performs a multi-level network traffic analysis, gathering as many features of network traffic as necessary. In an evaluation with 8,312 benign apps and 5,560 malware samples, TCP flow detection model and HTTP detection model all perform well and achieve detection rates of 98.16% and 99.65%, respectively. In addition, for the benefit of user, TrafficAV not only displays the final detection results, but also analyzes the behind-the- curtain reason of malicious results. This allows users to further investigate each feature’s contribution in the final result.

Published Paper   E-Certificate

"An effective detection of mobile malware behavior using network traffic :TrafficAV", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.4, Issue 6, page no.413 - 419, June-2019, Available :https://ijsdr.org/papers/IJSDR1906073.pdf

Volume 4 Issue 6, June-2019

Pages : 413 - 419

Paper Reg. ID: IJSDR_190689

Published Paper Id: IJSDR1906073

Downloads: 000347205

Research Area: Engineering

Country: gohana, Haryana, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR1906073

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR1906073

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition