期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：12
页码：1-10
出版社：Science and Information Society (SAI)
摘要：The increase of malware attacks may increase risk
in information technology industry such as Industrial Revolution
4.0 that consists of multiple sectors especially in cyber security.
Because of that malware detection technique plays vital role in
detecting malware attack that can give high impact towards the
cyber world. In accordance with the technique, one of
unsupervised machine learning able to detect malware attack by
identifying the behavior of the malware; which called clustering
technique. Owing to this matter, current research shows a paucity
of analysis in detecting malware behavior and limited source that
can be used in identifying malware attacks. Thus, this paper
introduce clustering detection model by using K-Means clustering
approach to detect malware behavior of data registry based on
the features of the malware. Clustering techniques that use
unsupervised algorithm in machine learning plays an important
role in grouping similar malware characteristics by studying the
behavior of the malware. Throughout the experiment, malware
features were selected and extracted from computer registry data
and eventually used in the proposed clustering detection model to
be clustered as normal or suspicious behavior. The results of the
experiment indicates that this proposed model is capable to
cluster normal and suspicious data into two separate groups with
high detection rate which is more than 90 percent accuracy.
Ultimately, the main contribution based on the findings is the
proposed framework can be used to cluster the data with the use
of data registry to detect malware.
关键词：Malware; malware detection; behavior analysis; kmeans
clustering; data registry
