适应性进化启发的算法用于生物信息系统的入侵检测
Mehtab Alam1, Chandra Kanta Samal1, Ashraf Ali2
1Department of Computer Science, Acharya Narendra Dev College, University of Delhi, Delhi, India.
Evolutionary bioinformatics online
|February 3, 2026
概括
本研究介绍了一种以进化为灵感的网络防御架构 (EICDA),以保护敏感的生物信息数据免受网络威胁. EICDA使用进化原理进行适应性检测和响应,增强生物医学平台的网络安全.
科学领域:
- 生物信息学是一种生物信息学.
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 生物信息系统由于与云,人工智能和实时分析集成而面临越来越多的网络安全威胁.
- 敏感的基因组和临床数据容易受到过,注射攻击和模型中毒的影响.
- 现有的防御机制很难适应生物信息学中不断变化的网络威胁.
研究的目的:
- 介绍一种以进化为灵感的网络防御架构 (EICDA),以加强生物信息学领域的网络安全.
- 开发一种适应性和弹性防御机制,模仿生物免疫和进化过程.
- 保护敏感的基因组和临床数据免受复杂的网络入侵.
主要方法:
- 开发了EICDA,集成基于遗传算法的检测核心与强化驱动的适应.
- 利用进化和生物免疫系统原则进行实时学习和重新配置.
- 在NSL-KDD上验证了EICDA,CICIDS2017以及模拟的基因组API日志环境.
主要成果:
- 实现了96.2%的检测准确率和2.8%的假阳性率.
- 证明了150ms以下的响应延迟,优于SVM,随机森林和CNN等传统方法.
- 展示了卓越的适应能力和稳健性,包括在威胁下抵御力.
结论:
- 对于下一代生物信息平台来说,EICDA提供了一个可行的,适应性的防御模型.
- 进化智能和网络安全的融合为关键的生物医学基础设施提供了强大的保护.
- EICDA代表了一种解决生物信息学中独特的网络安全挑战的新方法.
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