This paper is published in Volume-3, Issue-6, 2017
Area
Security and Machine Learning
Author
Vinu Thadevus Williams, Dr. K. S. Angel Viji
Org/Univ
College of Engineering Kidangoor, Kottayam, Kerala, India
Pub. Date
23 December, 2017
Paper ID
V3I6-1455
Publisher
Keywords
Online Learning, Malware Detection, Graph Kernels, Concept Drift.

Citationsacebook

IEEE
Vinu Thadevus Williams, Dr. K. S. Angel Viji. Android Malware Detection through Online Learning, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Vinu Thadevus Williams, Dr. K. S. Angel Viji (2017). Android Malware Detection through Online Learning. International Journal of Advance Research, Ideas and Innovations in Technology, 3(6) www.IJARIIT.com.

MLA
Vinu Thadevus Williams, Dr. K. S. Angel Viji. "Android Malware Detection through Online Learning." International Journal of Advance Research, Ideas and Innovations in Technology 3.6 (2017). www.IJARIIT.com.

Abstract

Android malware constantly evolves so as to evade detection. The entire malware population to be non-stationary. Contrary to this fact, most of the prior works on machine learning based android malware detection have assumed that the distribution of the observed malware characteristics (i.e., features) does not change over time. The problem of malware population drift and propose a novel online learning based framework to detect malware, named CASANDRA (Context-aware, Adaptive and Scalable Android malware detector). In order to perform accurate detection, a novel graph kernel that facilitates capturing apps security-sensitive behaviors along with their context information from dependence graphs is proposed. Besides being accurate and scalable, CASANDRA has specific advantages: first, being adaptive to the evolution in malware features over time; second, explaining the significant features that led to an apps classification as being malicious or benign.