Evaluation of secular changes in statistical features of traffic for the purpose of malware detection

Kenji Kawamoto*, Masatsugu Ichino, Mitsuhiro Hatada, Yusuke Otsuki, Hiroshi Yoshiura, Jiro Katto

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Applications and malware affecting them are dramatically changing. It isn't certain whether the currently used features can classify normal traffic or malware traffic correctly. In this paper, we evaluated the features used in previous studies while taking into account secular changes to classify normal traffic into the normal category and anomalous traffic into the anomalous category correctly. A secular change in this study is a difference in a feature between the date the training data were caputred and the date the test data were captured in the same circumstance. The evaluation is based on the Euclidean distance between the normal codebook or anomalous codebook made by vector quantization and the test data. We report on what causes these secular changes and which features with little or no secular change are effective for malware detection.

Original languageEnglish
Title of host publicationSoftware Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2012
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783642321719
Publication statusPublished - 2013

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X

ASJC Scopus subject areas

  • Artificial Intelligence


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