STS-AT:一个结构化的电压流对抗训练框架,用于强大的入侵检测
Juntong Zhu1, Zhihao Chen2, Rong Cong1
1Computer Science and Technology, School of Mathematics and Computer Science, Jilin Normal University, Siping 136000, China.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
本研究介绍了STS-AT,一种使用结构化张量和对抗训练的新网络入侵检测系统. 它显著提高了针对网络攻击的准确性和稳定性,同时缩短了培训时间.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 网络入侵检测对于网络安全至关重要.
- 目前的方法受到手动特征工程的影响,并且容易受到对抗性攻击的攻击.
- 深度学习模型往往会丢失歧视性信息,容易受到复杂的威胁.
研究的目的:
- 提出STS-AT,一种新的网络入侵检测方法.
- 解决手动功能工程和当前系统中的对抗性漏洞的局限性.
- 为了提高网络入侵检测的准确性,稳定性和效率.
主要方法:
- 结构化张量编码,将原始流量转换为数值表示.
- 一个分层的深度学习模型,将CNN和LSTM结合起来,用于空间-时间特征学习.
- 多策略对抗训练,以提高对攻击的模型稳定性.
主要成果:
- 在CICIDS2017数据集上的正常流量分类中实现了99.6%的准确性.
- 显著优于随机森林 (93.1%) 和支持矢量机器 (84.7%).
- 对抗敌对攻击的防御精度增加到96.8%以上,而无防御模型的精度为24.4%,训练时间减少了67.6%.
结论:
- 结构化张量编码有效地保留了原始的交通信息.
- 层次模型使全面的功能学习成为可能.
- 多策略对抗训练提高了效率,并确保了对抗网络威胁的强有力的防御.
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