Multi-label Positive and Unlabeled Learning and its Application to Common Vulnerabilities and Exposure Categorization

Masaki Aota, Tao Ban, Takeshi Takahashi, Noboru Murata

研究成果: Conference contribution

抄録

The widely adopted Common Weakness Enumeration (CWE), which stores and manages software and hardware vulnerability reports known as Common Vulnerabilities and Exposures (CVE) in a hierarchical structure, provides common baseline standard for weakness identification, mitigation, and prevention efforts. In this paper, we propose a machine-learning based method to assign pertinent CWE identifiers to new CVE entries. The proposed method formulates the task as a multi-label classification problem and exploits positive and unlabeled learning to address the lack of multi-labelled samples in learning. In evaluations, the proposed method demonstrated preferable performance compared to traditional multi-label classifiers. In particular, case studies demonstrated that multiple CWE iden-tifiers assigned to CVE entries carry essential information that can benefit security practices.

本文言語English
ホスト出版物のタイトルProceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021
編集者Liang Zhao, Neeraj Kumar, Robert C. Hsu, Deqing Zou
出版社Institute of Electrical and Electronics Engineers Inc.
ページ988-996
ページ数9
ISBN(電子版)9781665416580
DOI
出版ステータスPublished - 2021
イベント20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021 - Shenyang, China
継続期間: 2021 10月 202021 10月 22

出版物シリーズ

名前Proceedings - 2021 IEEE 20th International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021

Conference

Conference20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2021
国/地域China
CityShenyang
Period21/10/2021/10/22

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • 情報システム
  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理

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