植物和沙兰灵感网络攻击检测和数据恢复模型.
Rupam Kumar Sharma1, Biju Issac2, Qin Xin3
1Department of Computer Science and Engineering, Rajiv Gandhi University, Itanagar 791112, India.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
生物灵感模型提供进化的网络防御. 这项研究探讨了植物防御机制和的再生,用于强大的网络攻击检测和数据恢复系统,优于传统方法.
科学领域:
- 网络安全 网络安全
- 生物启发的计算技术
- 网络防御机制 网络防御机制
背景情况:
- 越来越多的互联网用户和网络攻击需要先进的防御策略.
- 传统的机器学习模型面临着数据和计算需求的局限性.
- 生物灵感模型为不断变化的威胁提供了适应性,低计算解决方案.
研究的目的:
- 探索植物进化的防御机制,以适应网络攻击.
- 调查再生模型,比如沙兰四肢再生,用于网络恢复.
- 开发一个强大的,生物灵感模型,用于网络攻击检测和数据恢复.
主要方法:
- 对已知和未知的外部攻击分析工厂防御策略.
- 基于生物再生原则建模网络恢复系统.
- 将拟议的生物灵感模型与Snort等入侵检测系统 (IDS) 和Burp和Cassandra等数据恢复工具的性能进行比较.
主要成果:
- 展示了生物灵感模型的潜力,以适应和发展防御机制.
- 展示了再生原理在创建自动化网络恢复系统中的有效性.
- 在检测和恢复方面实现了与现有的网络安全工具相比或优于现有的性能.
结论:
- 生物启发的方法为开发弹性和适应性网络安全系统提供了一个有希望的途径.
- 植物防御和再生为网络攻击检测和勒索软件恢复提供了新的策略.
- 拟议的模型显示了在低计算环境中增强网络安全的潜力.
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