Exploration into gray area: Efficient labeling for malicious domain name detection

Naoki Fukushi, Daiki Chiba, Mitsuaki Akiyama, Masato Uchida

研究成果: Conference contribution

1 引用 (Scopus)

抜粋

This paper presents a method to reduce the labeling cost when acquiring training data for a system that detects malicious domain names by supervised machine learning. The conventional system requires large quantities of both benign and malicious domain names to be prepared as training data to obtain a classifier with high classification accuracy. In general, malicious domain names are observed less frequently than benign domain names. Therefore, it is difficult to acquire a large number of malicious domain names without a dedicated labeling method. We propose a method based on active learning that labels data around the decision boundary of classification, i.e., in the gray area, and we show that the classification accuracy can be improved by only using approximately 2.5% of the training data used by the conventional system. An additional disadvantage of the conventional system is that, if the classifier is trained with a small amount of training data, its generalization ability cannot be guaranteed. We propose a method based on ensemble learning that integrates multiple classifiers, and we show that the classification accuracy can be stabilized and improved.

元の言語English
ホスト出版物のタイトルProceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019
編集者Vladimir Getov, Jean-Luc Gaudiot, Nariyoshi Yamai, Stelvio Cimato, Morris Chang, Yuuichi Teranishi, Ji-Jiang Yang, Hong Va Leong, Hossian Shahriar, Michiharu Takemoto, Dave Towey, Hiroki Takakura, Atilla Elci, Susumu Takeuchi, Satish Puri
出版者IEEE Computer Society
ページ770-775
ページ数6
ISBN(電子版)9781728126074
DOI
出版物ステータスPublished - 2019 7
イベント43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019 - Milwaukee, United States
継続期間: 2019 7 152019 7 19

出版物シリーズ

名前Proceedings - International Computer Software and Applications Conference
1
ISSN(印刷物)0730-3157

Conference

Conference43rd IEEE Annual Computer Software and Applications Conference, COMPSAC 2019
United States
Milwaukee
期間19/7/1519/7/19

ASJC Scopus subject areas

  • Software
  • Computer Science Applications

フィンガープリント Exploration into gray area: Efficient labeling for malicious domain name detection' の研究トピックを掘り下げます。これらはともに一意のフィンガープリントを構成します。

  • これを引用

    Fukushi, N., Chiba, D., Akiyama, M., & Uchida, M. (2019). Exploration into gray area: Efficient labeling for malicious domain name detection. : V. Getov, J-L. Gaudiot, N. Yamai, S. Cimato, M. Chang, Y. Teranishi, J-J. Yang, H. V. Leong, H. Shahriar, M. Takemoto, D. Towey, H. Takakura, A. Elci, S. Takeuchi, & S. Puri (版), Proceedings - 2019 IEEE 43rd Annual Computer Software and Applications Conference, COMPSAC 2019 (pp. 770-775). [8754122] (Proceedings - International Computer Software and Applications Conference; 巻数 1). IEEE Computer Society. https://doi.org/10.1109/COMPSAC.2019.00114