Research on filter-based adversarial feature selection against evasion attacks

Qimeng HUANG; Miaomiao WU; Yun LI

doi:10.11959/j.issn.1000-0801.2023140

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Research on filter-based adversarial feature selection against evasion attacks

Research and Development | 更新时间：2024-06-05

- Research on filter-based adversarial feature selection against evasion attacks
- Telecommunications Science Vol. 39, Issue 7, Pages: 46-58(2023)
- 作者机构：
  
  1. 南京邮电大学，江苏南京 210023
  2. 江苏省大数据安全与智能处理重点实验室，江苏南京 210023
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023140
  CLC： TP393
- Published Online：2023-07，
  
  Published：20 July 2023
- 稿件说明：
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Qimeng HUANG, Miaomiao WU, Yun LI. Research on filter-based adversarial feature selection against evasion attacks[J]. Telecommunications science, 2023, 39(7): 46-58.
DOI：

Qimeng HUANG, Miaomiao WU, Yun LI. Research on filter-based adversarial feature selection against evasion attacks[J]. Telecommunications science, 2023, 39(7): 46-58. DOI： 10.11959/j.issn.1000-0801.2023140.

摘要

随着机器学习技术的高速发展和大规模应用，其安全性越来越受关注，对抗性机器学习成为研究热点。在对抗性环境中，机器学习技术面临着被攻击的威胁，如垃圾邮件检测、交通信号识别、网络入侵检测等，攻击者通过篡改少量样本诱使分类器做出错误的分类决策，从而产生严重后果。基于最大相关最小冗余（mRMR），并考虑对抗逃避攻击的安全度量，设计了过滤式对抗特征选择的评价准则。此外，还基于分解策略的多目标演化子集选择（DPOSS）算法，提出一种鲁棒性对抗特征选择算法 SDPOSS，其不依赖后续模型，且能有效处理大规模高维特征。实验结果表明，随着分解个数的增加，SDPOSS 的运行时间会线性下降，且获得很好的分类性能。同时，SDPOSS算法在逃避攻击下的鲁棒性较好，为对抗性机器学习提供了新的思路。

Abstract

With the rapid development and widespread application of machine learning technology

its security has attracted increasing attention

leading to a growing interest in adversarial machine learning.In adversarial scenarios

machine learning techniques are threatened by attacks that manipulate a small number of samples to induce misclassification

resulting in serious consequences in various domains such as spam detection

traffic signal recognition

and network intrusion detection.An evaluation criterion for filter-based adversarial feature selection was proposed

based on the minimum redundancy and maximum relevance (mRMR) method

while considering security metrics against evasion attacks.Additionally

a robust adversarial feature selection algorithm was introduced

named SDPOSS

which was based on the decomposition-based Pareto optimization for subset selection (DPOSS) algorithm.SDPOSS didn’t depend on subsequent models and effectively handles large-scale high-dimensional feature spaces.Experimental results demonstrate that as the number of decompositions increases

the runtime of SDPOSS decreases linearly

while achieving excellent classification performance.Moreover

SDPOSS exhibits strong robustness against evasion attacks

providing new insights for adversarial machine learning.

关键词

Keywords

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