信息网络安全 ›› 2020, Vol. 20 ›› Issue (12): 19-27.doi: 10.3969/j.issn.1671-1122.2020.12.003
收稿日期:
2020-07-13
出版日期:
2020-12-10
发布日期:
2021-01-12
通讯作者:
韩松
E-mail:418594110@qq.com
作者简介:
何泾沙(1961—),男,陕西,教授,博士,主要研究方向为网络安全、测试与分析、云计算|韩松(1994—),男,陕西,硕士,主要研究方向为信息安全|朱娜斐(1981—),女,河南,副教授,博士,主要研究方向为网络安全、隐私保护和区块链|葛加可(1993—),男,山西,博士研究生,主要研究方向为信息安全
基金资助:
HE Jingsha1, HAN Song2(), ZHU Nafei1, GE Jiake3
Received:
2020-07-13
Online:
2020-12-10
Published:
2021-01-12
Contact:
HAN Song
E-mail:418594110@qq.com
摘要:
随着互联网用户数量的剧增,网络威胁也在迅速增长,传统的被动防御措施不足以防御日益多变的网络入侵。传统入侵检测系统原理是收集病毒特征再进行特征匹配,对于未知病毒,传统检测机制存在滞后性。面对日益繁杂的网络安全环境,研究基于人工免疫理论的入侵检测系统具有重要意义。文章首先介绍人工免疫理论的核心思想否定选择算法,进而介绍实值否定选择算法和V-detector算法。针对V-detector算法的不足,进行3个方面的改进:提出基于定距变异的克隆选择算法提高检测器生成效率;提出去冗算法减少检测器冗余,加快算法收敛;引入并改进假设检验方法,对检测器集合的覆盖率进行评估。实验证明,文章提出的改进V-detector算法能有效提升检测精度,减少检测黑洞,并大大缩减检测时间。
中图分类号:
何泾沙, 韩松, 朱娜斐, 葛加可. 基于改进V-detector算法的入侵检测研究与优化[J]. 信息网络安全, 2020, 20(12): 19-27.
HE Jingsha, HAN Song, ZHU Nafei, GE Jiake. Research and Optimization of Intrusion Detection Based on Improved V-detector Algorithm[J]. Netinfo Security, 2020, 20(12): 19-27.
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