Accuracy Comparison among Different Machine Learning Techniques for Detecting Malicious Codes

被引：0
|
作者：
Narang, Komal [1 ]
机构：
[1] Bangkok Univ, Sch Sci & Technol, Multimedia Intelligent Technol Lab BU MIT, Bangkok, Thailand
来源：
PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS (ICCS-2015) | 2016年 / 1715卷
关键词：
D O I：
10.1063/1.4942738
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
in this paper, a machine learning based model for malware detection is proposed. It can detect newly released malware i.e. zero day attack by analyzing operation codes on Android operating system. The accuracy of Na ve Bayes, Support Vector Machine (SVM) and Neural Network for detecting malicious code has been compared for the proposed model. In the experiment 400 benign files, 100 system files and 500 malicious files have been used to construct the model. The model yields the best accuracy 88.9% when neural network is used as classifier and achieved 95% and 82.8% accuracy for sensitivity and specificity respectively.
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页数：4
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