Abstract
According to a Recent F-Secure Report, 97% of Mobile Malware is Designed for the Android Platform Which Has a Growing Number of Consumers. in Order to Protect Consumers from Downloading Malicious Applications, There Should Be an Effective System of Malware Classification that Can Detect Previously Unseen Viruses. in This Paper, We Present a Scalable and Highly Accurate Method for Malware Classification based on Features Extracted from Android Application Package (APK) Files. We Explored Several Techniques for Tackling Independence Assumptions in Naive Bayes and Proposed Normalized Bernoulli Naive Bayes Classifier that Resulted in an Improved Class Separation and Higher Accuracy. We Conducted a Set of Experiments on an Up-To-Date Large Dataset of APKs Provided by F-Secure and Achieved 0.1% False Positive Rate with overall Accuracy of 91%.
Recommended Citation
L. Sayfullina et al., "Efficient Detection of Zero-Day Android Malware using Normalized Bernoulli Naive Bayes," Proceedings - 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2015, vol. 1, pp. 198 - 205, article no. 7345283, Institute of Electrical and Electronics Engineers, Dec 2015.
The definitive version is available at https://doi.org/10.1109/Trustcom.2015.375
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Malware Classification; Naive Bayes; Security in Android
International Standard Book Number (ISBN)
978-146737951-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
02 Dec 2015
