EHO优化算法在网络安全检测中的应用  

Application of EHO Optimization Algorithm in Network Security Detection

作　　者：郦丽华[1] LI Lihua(Zhejiang Yuying Vocational and Technical College Information Technology Branch Zhejiang,Hangzhou 310018,China)

机构地区：[1]浙江育英职业技术学院信息技术分院,浙江杭州310018

出　　处：《佳木斯大学学报（自然科学版）》2024年第9期153-156,共4页Journal of Jiamusi University：Natural Science Edition

摘　　要：随着网络资源和用户数量的快速增长,网络安全问题变得更加复杂和难以解决。为了应对这些问题,研究提出了改进的象群优化算法,并构建了一种新的入侵检测模型。实验结果表明,该模型在四种攻击类型的数据上取得了最大适应度值,明显优于其他算法。与改进前的算法相比,改进后的方法在入侵攻击类型的检测正确率上平均提升了3.65%,7.56%,9.42%,10.13%和2.96%,同时降低了平均误报率5.75%,8.43%,10.03%,3.82%和5.01%。综上所述,改进后的方法在入侵检测领域展现出卓越的性能和泛化能力。With the rapid growth of network resources and the number of users,network security problems have become more complex and difficult to solve.To cope with these problems,this study proposes an improved elephant swarm optimization algorithm and constructs a new intrusion detection model.The experimental results show that the model achieves the maximum fitness value on the data of four attack types,which is significantly better than other algorithms.Compared with the algorithm be-fore the improvement,the improved method improves the correct detection rate of intrusion attack types by 3.65%,7.56%,9.42%,10.13%,and 2.96%on average,while reducing the average false alarm rate by 5.75%,8.43%,10.03%,3.82%,and 5.01%.In summary,the improved method demon-strates excellent performance and generalization capability in the field of intrusion detection.

关 键 词：象群优化算法 入侵检测 特征选择 参数优化 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]