应用防务模型来加强金融服务部门计算机网络中的人工智能信息安全
Arodh Lal Karn1, Hayder M A Ghanimi2,3, Vijayalakshmi Iyengar4
1Department of Financial and Actuarial Mathematics, School of Mathematics and Physics, Xian Jiaotong-Liverpool University, Suzhou City, Jiangsu Province, 215123, P.R. China.
Scientific reports
|August 19, 2025
概括
本研究介绍了一种人工智能增强的深度防御 (AI-E-DiD) 模型,用于打击金融网络中复杂的网络威胁. AI-E-DiD模型显著提高了先进网络攻击的检测准确度和响应时间.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 金融技术 金融技术
背景情况:
- 金融服务部门 (FSS) 的数字化提高了运营效率,但使其面临APT和DoS攻击等先进的网络威胁.
- 传统的入侵检测系统 (IDS) 面临着高的假阳性率 (FPR) 和缺乏适应现代网络威胁环境的能力.
- 现有的网络安全措施不足以应对针对金融网络的威胁的不断变化和复杂性.
研究的目的:
- 开发和评估一个人工智能增强的深度防御 (AI-E-DiD) 模型,用于金融网络中的实时,自适应和可扩展的网络安全预防.
- 通过智能和动态的安全措施,提高金融机构对复杂的网络威胁的弹性.
- 通过提出人工智能驱动的方法来解决传统IDS的局限性,以改进威胁检测和缓解.
主要方法:
- 混合生成对抗网络和长短期内存自动编码器 (GAN-LSTM-AE) 的集成,用于智能异常检测.
- 在Galois/Counter Mode (AES-GCM) 中实施高级加密标准,以确保数据完整性和机密性.
- 开发一个人工智能增强的入侵防范系统 (AI-E-IPS),用于动态威胁缓解和应对.
主要成果:
- 对NSL-KDD和CICIDS-2017数据集的实证评估显示,检测准确度高 (DoS为95.6%,DDoS为94.2%) 和响应时间低 (<0.25秒).
- NS-3模拟表明,AI-E-DiD在检测速率 (DR),计算开销 (CO) 和网络吞吐量 (NT) 方面超过了传统的IDS和传统的深度防御 (DiD).
- 该模型在不同的用户负载,攻击类型和数据大小中表现出强大的性能,证实了其可扩展性.
结论:
- AI-E-DiD模型为金融网络中的实时,自适应和可扩展的网络安全提供了一个有希望的解决方案.
- 拟议的混合人工智能方法有效地解决了传统网络安全系统在检测和减轻高级威胁方面的局限性.
- 调查结果支持AI-E-DiD在高风险的金融环境中潜在的部署,要求有弹性和智能网络安全基础设施.
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