改进的飞蛾扑火优化算法在网络入侵检测系统中的应用

doi:10.11772/j.issn.1001-9081.2018041315

计算机应用 ›› 2018, Vol. 38 ›› Issue (11): 3231-3235.DOI: 10.11772/j.issn.1001-9081.2018041315

• 第七届中国数据挖掘会议(CCDM 2018) • 上一篇下一篇

改进的飞蛾扑火优化算法在网络入侵检测系统中的应用

徐慧, 方策, 刘翔, 叶志伟

湖北工业大学计算机学院, 武汉 430068

收稿日期:2018-04-14 修回日期:2018-06-27 出版日期:2018-11-10 发布日期:2018-11-10
通讯作者: 徐慧
作者简介:徐慧(1983-),女,湖北武汉人,副教授,博士,CCF会员,主要研究方向:网络与服务管理;方策(1994-),男,湖北黄冈人,硕士研究生,主要研究方向:网络安全、数据挖掘;刘翔(1993-),男,湖北武汉人,硕士研究生,主要研究方向:网络安全、优化算法;叶志伟(1978-),男,湖北浠水人,教授,博士,主要研究方向:智能计算、机器学习。
基金资助:
国家自然科学基金资助项目（61602162，61440024）。

Network intrusion detection system based on improved moth-flame optimization algorithm

XU Hui, FANG Ce, LIU Xiang, YE Zhiwei

School of Computer Science, Hubei University of Technology, Wuhan Hubei 430068, China

Received:2018-04-14 Revised:2018-06-27 Online:2018-11-10 Published:2018-11-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61602162, 61440024).

摘要/Abstract

摘要： 针对当前网络入侵检测中的数据量较大、数据维度较高的特点，将飞蛾扑火优化（MFO）算法应用于网络入侵检测的特征选择中。鉴于MFO算法收敛过快、易陷入局部最优的问题，提出一种融合粒子群优化（PSO）的二进制飞蛾扑火优化（BPMFO）算法。该算法引入MFO螺旋飞行公式，具有较强的局部搜索能力；结合了粒子群优化（PSO）算法的速度更新方法，让种群个体随着全局最优解和历史最优解的方向移动，增强算法的全局收敛性，从而避免易陷入局部最优。仿真实验以KDD CUP 99数据集为实验基础，分别采用支持向量机（SVM）、K最近邻（KNN）算法和朴素贝叶斯（NBC）3种分类器，与二进制飞蛾扑火优化（BMFO）算法、二进制粒子群优化（BPSO）算法、二进制遗传算法（BGA）、二进制灰狼优化（BGWO）算法和二进制布谷鸟搜索（BCS）算法进行了实验对比。实验结果表明，BPMFO算法应用于网络入侵检测的特征选择时，在算法精度、运行效率、稳定性、收敛速度以及跳出局部最优的综合性能上具有明显优势。

关键词: 网络入侵检测, 特征选择, 飞蛾扑火优化算法, 粒子群优化算法, 融合

Abstract: Due to a large amount of data and high dimension in currently network intrusion detection, a Moth-Flame Optimization (MFO) algorithm was applied to the feature selection of network intrusion detection. Since MFO algorithm converges fast and easy falls into local optimum, a Binary Moth-Flame Optimization integrated with Particle Swarm Optimization (BPMFO) algorithm was proposed. On one side, the spiral flight formula of the MFO algorithm was introduced to obtain strong local search ability. On the other side, the speed updating formula of the Particle Swarm Optimization (PSO) algorithm was combined to make the individual to move in the direction of global optimal solution and historical optimal solution, in order to increase the global convergence and avoid to fall into local optimum. By adopting KDD CUP 99 data set as the experimental basis, using three classifiers of Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naive Bayesian Classifier (NBC), Binary Moth-Flame Optimization (BMFO), Binary Particle Swarm Optimization (BPSO), Binary Genetic Algorithm (BGA), Binary Grey Wolf Optimization (BGWO) and Binary Cuckoo Search (BCS) were compared in the experiment. The experimental results show that, BPMFO algorithm has obvious advantages in the comprehensive performance including algorithm accuracy, operation efficiency, stability, convergence speed and jumping out of local optima when it is applied to the feature selection of network intrusion detection.

Key words: network intrusion detection, feature selection, Moth-Flame Optimization (MFO) algorithm, Particle Swarm Optimization (PSO) algorithm, integration

中图分类号:

TP393.08

徐慧, 方策, 刘翔, 叶志伟. 改进的飞蛾扑火优化算法在网络入侵检测系统中的应用[J]. 计算机应用, 2018, 38(11): 3231-3235.

XU Hui, FANG Ce, LIU Xiang, YE Zhiwei. Network intrusion detection system based on improved moth-flame optimization algorithm[J]. Journal of Computer Applications, 2018, 38(11): 3231-3235.

参考文献

[1] BEGHDAD R. Modelling and solving the intrusion detection problem in computer networks[J]. Computers & Security, 2004, 23(8):687-696.
[2] 戴远飞,陈星,陈宏,等.基于特征选择的网络入侵检测方法[J].计算机应用研究,2017,34(8):2429-2433.(DAI Y F, CHEN X, CHEN H, et al. Computer application research based on feature selection network intrusion detection method[J].Application Research of Computers, 2017,34(8):2429-2433.)
[3] KENNEDY J, EBERHART R. Particle swarm optimization[C]//Proceedings of the 1995 IEEE International Conference on Neural Networks. Piscataway, NJ:IEEE, 2002:1942-1948.
[4] BATAINEH M A, AL-QUDAH Z. A novel gene identification algorithm with Bayesian classification[J]. Biomedical Signal Processing and Control, 2017, 31:6-15.
[5] 陈昌帅. 二进制灰狼优化算法的研究与分析[J]. 信息系统工程, 2016(7):136-138.(CHEN C S. Research and analysis of binary gray wolf optimization algorithm[J]. Information Systems Engineering, 2016(7):136-138.)
[6] OUAARAB A, AHIOD B, YANG X S. Discrete cuckoo search algorithm for the travelling salesman problem[J]. Neural Computing & Applications, 2014, 24(7/8):1659-1669.
[7] MIRJALILI S. Moth-flame optimization algorithm:a novel nature-inspired heuristic paradigm[J]. Knowledge-Based Systems, 2015, 89:228-249.
[8] 李志明, 莫愿斌, 张森. 一种新颖的群智能算法:飞蛾扑火优化算法[J]. 电脑知识与技术, 2016, 12(31):172-176.(LI Z M, MO Y B, ZHANG S. A novel swarm intelligence algorithm:Moth-flame optimization algorithm[J]. Computer Knowledge and Technology, 2016, 12(31):172-176.)
[9] 李伟琨,阙波,王万良,等.基于多目标飞蛾算法的电力系统无功优化研究[J].计算机科学,2017,44(S2):503-509.(LI W K, QUE W, WANG W L, et al. Multi-objective moth-flame optimization algorithm based optimal reactive power dispatch for power system[J]. Computer Science,2017,44(S2):503-509.)
[10] SAVSANI V, TAWHID M A. Non-dominated Sorting Moth Flame Optimization (NS-MFO) for multi-objective problems[J]. Engineering Applications of Artificial Intelligence, 2017, 63:20-32.
[11] SINGH P, PRAKASH S. Optical network unit placement in Fiber-Wireless (FiWi) access network by moth-flame optimization algorithm[J]. Optical Fiber Technology, 2017, 36:403-411.
[12] YILDIZ B S, YILDIZ A R. Moth-flame optimization algorithm to determine optimal machining parameters in manufacturing processes[J]. Materials Testing, 2017, 59(5):425-429.
[13] 李志明, 莫愿斌. 基于Lévy飞行的飞蛾扑火优化算法[J].计算机工程与设计,2017,38(3):807-813.(LI Z M, MO Y B. Moth-flame optimization algorithm based on Lévy flights[J]. Computer Engineering and Design,2017,38(3):807-813.)
[14] 吴伟民,李泽熊,林志毅,等.飞蛾纵横交叉混沌捕焰优化算法[J].计算机工程与应用,2018,54(3):136-141.(WU W M, LI Z X, LIN Z Y, et al. Multi-objective moth-flame optimization algorithm based optimal reactive power dispatch for power system[J]. Computer Engineering and Design, 2018,54(3):136-141.)
[15] 计智伟, 胡珉, 尹建新. 特征选择算法综述[J]. 电子设计工程, 2011, 19(9):46-51.(JI Z W, HU M, YIN J X. A summary of feature selection algorithms[J]. Electronic Design Engineering, 2011, 19(9):46-51.)
[16] UKIL A. Support vector machine[J]. Computer Science, 2002, 1(4):1-28.
[17] PETERSON L E. K-nearest neighbor[J]. Scholarpedia, 2009, 4(2): 1883.
[18] LANGLEY P, THOMPSON K. An analysis of Bayesian classifiers[C]// Proceedings of the 10th National Conference on Artificial Intelligence. San Jose, California: AAAI, 1992:223-228.
[19] 郭春. 基于数据挖掘的网络入侵检测关键技术研究[D]. 北京:北京邮电大学, 2014.(GUO C. Research on key technology of network intrusion detection based on data mining [D]. Beijing: Beijing University of Posts and Telecommunications, 2014.
[20] HOLMES G, DONKIN A, WITTEN I H. WEKA: a machine learning workbench[C]// Proceedings of the 2nd Australian and New Zealand Conference on Intelligent Information Systems. Piscataway, NJ: IEEE, 2002:357-361.

改进的飞蛾扑火优化算法在网络入侵检测系统中的应用

Network intrusion detection system based on improved moth-flame optimization algorithm

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	湛航, 何朗, 黄樟灿, 李华峰, 张蔷, 谈庆. 改进的基于层次距离的基因表达式编程特征选择分类算法[J]. 计算机应用, 2021, 41(9): 2658-2667.
[2]	祝承, 赵晓琦, 赵丽萍, 焦玉宏, 朱亚飞, 陈建英, 周伟, 谭颖. 基于谱聚类半监督特征选择的功能磁共振成像数据分类[J]. 计算机应用, 2021, 41(8): 2288-2293.
[3]	李蒙蒙, 秦伟, 刘艺, 刁兴春. 结合头脑风暴优化的混合蚁群优化算法[J]. 计算机应用, 2021, 41(8): 2412-2417.
[4]	周险兵, 樊小超, 任鸽, 杨勇. 基于多层次语义特征的英文作文自动评分方法[J]. 计算机应用, 2021, 41(8): 2205-2211.
[5]	王伟, 赵尔平, 崔志远, 孙浩. 基于HowNet义原和Word2vec词向量表示的多特征融合消歧方法[J]. 计算机应用, 2021, 41(8): 2193-2198.
[6]	张盟, 郭健全. 需求和回收不确定的闭环供应链渠道结构选择[J]. 计算机应用, 2021, 41(7): 2100-2107.
[7]	张林发, 张榆锋, 王琨, 李支尧. 基于直觉模糊集和亮度增强的医学图像融合[J]. 计算机应用, 2021, 41(7): 2082-2091.
[8]	李朝, 兰海, 魏宪. 基于注意力的毫米波-激光雷达融合目标检测[J]. 计算机应用, 2021, 41(7): 2137-2144.
[9]	吴丽丹, 薛雨阳, 童同, 杜民, 高钦泉. 基于前景语义信息的图像着色算法[J]. 计算机应用, 2021, 41(7): 2048-2053.
[10]	杜炎, 吕良福, 焦一辰. 基于模糊推理的模糊原型网络[J]. 计算机应用, 2021, 41(7): 1885-1890.
[11]	章荪, 尹春勇. 基于多任务学习的时序多模态情感分析模型[J]. 计算机应用, 2021, 41(6): 1631-1639.
[12]	牛康力, 谌雨章, 沈君凤, 曾张帆, 潘永才, 王绎冲. 基于深度学习的双通道夜视图像复原方法[J]. 计算机应用, 2021, 41(6): 1775-1784.
[13]	贾鹤鸣, 姜子超, 李瑶, 孙康健. 基于改进斑点鬣狗优化算法的同步优化特征选择[J]. 计算机应用, 2021, 41(5): 1290-1298.
[14]	栾佳宁, 张伟, 孙伟, 张奥, 韩冬. 基于二维码视觉与激光雷达融合的高精度定位算法[J]. 计算机应用, 2021, 41(5): 1484-1491.
[15]	林筠超, 万源. 基于图结构优化的自适应多度量非监督特征选择方法[J]. 计算机应用, 2021, 41(5): 1282-1289.