节能入侵检测采用一个协议意识的变压器升混合动力模型
M Ganesh Karthik1, Vijay Keerthika2, Srihari Varma Mantena3
1GITAM School of Computer Science and Engineering, GITAM University-Bengaluru Campus, Bengaluru, India.
Scientific reports
|February 3, 2026
概括
本研究介绍了用于高效入侵检测系统 (IDS) 的变压器增强尖端神经网络 (TASNN). TASNN提高了准确性并降低了计算成本,特别是在网络流量的罕见攻击中.
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:
- 深度学习和变压器模型对入侵检测有希望,但面临着计算成本,能源效率和数据不平衡的挑战.
- 现有的方法在异质网络流量模式中难以检测罕见的攻击类.
研究的目的:
- 为入侵检测系统 (IDS) 提出一种新的变压器增强尖端神经网络 (TASNN).
- 为了提高对多样化的网络流量的稳定性,并改善罕见攻击类别的检测.
- 减少基于边缘的IDS的计算开销和提高能源效率.
主要方法:
- 开发了TASNN,将注意力机制与节能尖端神经网络相结合.
- 集成的协议意识的自适应规范化 (PAAN) 和伪流重建 (PFR) 为交通模式的稳定性.
- 利用自适应尖峰编码 (MASE,EDC) 和交叉模式网关 (XMG) 进行高效的特征表示和动态调节.
- 雇员尖端意识信息融合 (SAIF) 提供稳定和可解释的特征选择.
主要成果:
- 与现有方法相比,TASNN在基准数据集上展示了更好的分类性能.
- 拟议的模型实现了减少计算开销和提高能源效率.
- 塔斯恩显示出卓越的检测能力,特别是在不平衡的网络流量中的罕见攻击类.
结论:
- 塔斯恩为入侵检测提供了一个有前途的解决方案,将高精度与计算和能源效率相平衡.
- 该框架非常适合资源有限的环境和基于边缘的入侵检测场景.
- 尖端计算和注意力机制的整合为网络安全提供了强大而高效的方法.
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