On Embedding Backdoor in Malware Detectors Using Machine Learning

Shoichiro Sasaki, Seira Hidano, Toshihiro Uchibayashi, Takuo Suganuma, Masahiro Hiji, Shinsaku Kiyomoto

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

2 被引用数 (Scopus)

抄録

Researching for malware detection using machine learning is becoming active. However, conventional detection techniques do not consider the impact of attacks on machine learning, which has become complicated in recent years. In this research, we focus on data poisoning attack, which is one of the typical attacks on machine learning, and aim to clarify the influence of attacks on malware detection technology. Data poisoning attack is an attack method that intentionally manipulates the predicted result of a learned model by injecting poisoning data into training data, and by applying this, it is possible to embed a backdoor that induces mis-prediction of only specific input data. In this paper, we first propose an attack framework for backdoor embedding that prevents detection of only specific types of malware by data poisoning attack. Next, we will describe a method to generate poisoning data efficiently while avoiding attack detection by solving the optimization problem. Furthermore, we take malware detection technology using logistic regression and show the effectiveness of the our method through evaluation experiments using two datasets.

本文言語英語
ホスト出版物のタイトル2019 17th International Conference on Privacy, Security and Trust, PST 2019 - Proceedings
編集者Ali Ghorbani, Indrakshi Ray, Arash Habibi Lashkari, Jie Zhang, Rongxing Lu
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728132655
DOI
出版ステータス出版済み - 8月 2019
外部発表はい
イベント17th International Conference on Privacy, Security and Trust, PST 2019 - Fredericton, カナダ
継続期間: 8月 26 20198月 28 2019

出版物シリーズ

名前2019 17th International Conference on Privacy, Security and Trust, PST 2019 - Proceedings

会議

会議17th International Conference on Privacy, Security and Trust, PST 2019
国/地域カナダ
CityFredericton
Period8/26/198/28/19

!!!All Science Journal Classification (ASJC) codes

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

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