利用自我注意力驱动的封闭反复单元与鱼优化算法来检测网络攻击,使用联合学习框架
Manal Abdullah Alohali1, Hatim Dafaalla2, Mohammed Baihan3
1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Scientific reports
|July 3, 2025
概括
本研究介绍了一种使用联合学习 (FL) 和人工智能优化来有效检测网络攻击的新型网络安全方法. SAMFL-SCDCOA方法实现了99.04%的准确性,增强了实时威胁预防.
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:
- 网络安全对于保护数据免受恶意软件和DoS攻击等各种网络攻击至关重要.
- 人工智能 (AI) 和机器学习 (ML) 为加强网络安全防御提供了新的技术.
- 联合学习 (FL) 解决了在ML模型中的安全性,数据隐私和访问权限方面的挑战.
研究的目的:
- 提出一个有效的实时网络攻击预防方法,使用联合学习和先进的优化算法.
- 引入自我注意机制驱动的联合学习,以使用鱼优化算法 (SAMFL-SCDCOA) 安全检测网络攻击.
主要方法:
- 使用Z-score规范化进行数据预处理,以获得一致性和准确性.
- 使用鱼优化算法 (COA) 进行特征选择 (FS).
- 网络安全分类使用一个带有自我注意力的门式循环单元 (GRU-SA) 模型,由改进的鱼优化算法 (IPOA) 优化.
主要成果:
- 在网络攻击检测方面,SAMFL-SCDCOA方法论表现出了卓越的性能.
- 在CICIDS-2017数据集上的实验验证证证了该技术的有效性.
- 实现了99.04%的分类准确度,超过了现有模型.
结论:
- 拟议的SAMFL-SCDCOA方法为实时网络攻击检测提供了一个强大的解决方案.
- 整合FL,AI优化和先进的ML模型显著提高了网络安全.
- 这种方法为保护大规模系统免受不断变化的网络威胁提供了一个有希望的方向.
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