利用可解释的人工智能在大型网络环境中早期检测和减轻网络威胁
G Nalinipriya1, S Rama Sree2, K Radhika3
1Department of Information Technology, Saveetha Engineering College, Chennai, 602 105, India.
Scientific reports
|July 9, 2025
概括
本研究引入了一种使用可解释的人工智能 (XAI) 进行早期网络威胁检测的新方法. 该方法显著提高了在大型网络中识别和分类网络攻击的准确性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 网络威胁正在迅速发展,挑战传统的安全措施.
- 机器学习 (ML) 为恶意软件检测提供了可扩展和自动化的解决方案.
- 现有的ML方法需要有效处理复杂的网络安全挑战.
研究的目的:
- 开发一种用于在大型网络环境中早期检测和减轻网络威胁的新方法.
- 提高网络攻击分类的准确性和可解释性.
- 解决针对复杂威胁的传统网络安全解决方案的局限性.
主要方法:
- 提出了利用可解释的人工智能在大型网络环境中早期检测和缓解网络威胁的方法 (LXAIDM-CTLSN).
- 为了数据标准化,使用了Min-max规范化.
- 采用Mayfly优化算法 (MOA) 进行特征选择和徒步优化算法 (HOA) 进行Sparse Denoising Autoencoder (SDAE) 模型的超参数调整.
- 整合LIME (局部可解释模型-不可知解释) 进行解释.
主要成果:
- 在对网络攻击进行分类时,LXAIDM-CTLSN方法取得了99.09%的卓越准确性.
- 在检测和减轻网络威胁方面表现出更好的稳定性和效率.
- 在NSLKDD2015和CICIDS2017数据集上验证了性能.
结论:
- 开发的LXAIDM-CTLSN方法通过可解释的AI有效地提高了网络安全.
- 优化算法和SDAE的整合为威胁检测提供了一个强大的框架.
- 通过XAI集成,可以提高网络威胁分类系统的理解和可靠性.
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