一个改进的长期短期内存网络用于入侵检测检测
Asmaa Ahmed Awad1, Ahmed Fouad Ali1,2, Tarek Gaber3,1
1Department of Computer Science, Faculty of Computers and Informatics, Suez Canal University, Ismailia, Egypt.
PloS one
|August 1, 2023
概括
一个新的改进的长期短期记忆 (ILSTM) 算法增强了入侵检测系统. 与传统方法相比,这种新的方法显著提高了识别网络威胁的准确性和精确性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 侵入检测系统 (IDS) 对网络安全至关重要,可以识别来自网络流量的威胁.
- 传统的机器学习方法在IDS中经常与低准确度和高错误报警率作斗争.
- 像长期短期记忆 (LSTM) 这样的深度学习模型显示出希望,但需要广泛的培训.
研究的目的:
- 提出一种新的改进的长期短期记忆 (ILSTM) 算法,用于增强入侵检测.
- 提高网络入侵检测系统的准确性和减少虚假报警.
- 开发一种高效的IDS,能够处理网络攻击的二进制和多类分类.
主要方法:
- 开发了一个ILSTM算法,集成混乱蝶优化算法 (CBOA) 和粒子群优化 (PSO).
- 采用了两阶段的方法:最初的LSTM培训,然后是CBOA和PSO以优化体重.
- 在使用九个性能指标的NSL-KDD和LITNET-2020数据集上评估了基于ILSTM的IDS.
主要成果:
- 与标准的LSTM相比,ILSTM算法获得了明显更高的准确性 (93.09%) 和精度 (96.86%) (82.74%的准确性,76.49%的精度).
- 在两种数据集中,ILSTM在LSTM和其他深度学习算法上都表现出卓越的性能.
- 统计分析证实了ILSTM的更大意义,特别是在多种类型的入侵类型的多重分类中,如DoS,Prob和U2R.
结论:
- 拟议的ILSTM算法为网络入侵检测系统提供了实质性的改进.
- ILSTM有效地提高了准确性和精度,优于现有的深度学习模型.
- 这种优化的方法为识别各种网络入侵提供了更强大,更具统计意义的解决方案.
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