Malware Detection Using Machine Learning

被引：3

作者：

Kumar, Ajay ^{[1
]}

Abhishek, Kumar ^{[1
]}

Shah, Kunjal ^{[2
]}

Patel, Divy ^{[2
]}

Jain, Yash ^{[2
]}

Chheda, Harsh ^{[2
]}

Nerurka, Pranav ^{[2
,3
]}

机构：

[1] NIT Patna, Patna, Bihar, India

[2] Veermata Jijabai Technol Inst, Mumbai, Maharashtra, India

[3] NMIMS Mumbai, Mumbai, Maharashtra, India

来源：

KNOWLEDGE GRAPHS AND SEMANTIC WEB, KGSWC 2020 | 2020年 / 1232卷

关键词：

Security; Malware; Machine learning;

D O I：

10.1007/978-3-030-65384-2_5

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Decision making using Machine Learning can be efficiently applied to security. Malware has become a big risk in today's times. In order to provide protection for the same, we present a machine-learning based technique for predicting Windows PE files as benign or malignant based on fifty-seven of their attributes. We have used the Brazilian Malware dataset, which had around 1,00,000 samples and 57 labels. We have made seven models, and have achieved 99.7% accuracy for the Random Forest model, which is very high when compared to other existing systems. Thus using the Random Forest model one can make a decision on whether a particular file is malware or benign.

引用

页码：61 / 71

页数：11

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