改进果蝇算法优化加权极限学习机的入侵检测

doi:10.16182/j.issn1004731x.joss.19-0361

系统仿真学报 ›› 2021, Vol. 33 ›› Issue (2): 331-338.doi: 10.16182/j.issn1004731x.joss.19-0361

改进果蝇算法优化加权极限学习机的入侵检测

党建武, 谭凌

江西财经大学软件与物联网工程学院,江西南昌 330013

收稿日期:2019-07-29 修回日期:2019-11-28 出版日期:2021-02-18 发布日期:2021-02-20
通讯作者: 谭凌(1995-),女,硕士生,研究方向为信息安全。E-mail: tanling@jjccb.com
作者简介:党建武(1964-),男,博士,教授,研究方向为智能信息处理、信息安全、金融时间序列分析。E-mail: dangjianwu006@163.com

An Intrusion Detection Algorithm Based on IFOA and WELM

Dang Jianwu, Tan Ling

School of Software and Internet of Things Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China

Received:2019-07-29 Revised:2019-11-28 Online:2021-02-18 Published:2021-02-20

摘要/Abstract

摘要： 提出一种改进的果蝇算法优化加权极限学习机入侵检测算法,利用加权极限学习机训练时间短、泛化性能好等优点,对NSL-KDD入侵检测数据集中的不均衡现象,增加少数类攻击的权重,使对网络攻击中稀有攻击的检测率比传统机器学习方法有大幅提高;用迭代步长自适应调整的果蝇优化算法,对加权极限学习机中的隐含层输入权值和偏置进行全局寻优,以避免算法陷入局部最优解,实现了对NSL-KDD入侵检测数据集的分类。实验表明：本算法对稀有攻击的检测率和分类准确率均有提高,误报率有所降低。

关键词: 入侵检测, 不均衡数据集, 加权极限学习机, 果蝇优化算法

Abstract: An intrusion detection algorithm of WELM optimized by IFOA is proposed. The advantages of short training time and good generalization performance of WELM are used, and the weight of minority attacks is increased, so that the recall rate of minority attacks in network attacks is greatly improved.The FOA with adaptive adjustment of the iterative step size is used, so the input weights and bias of the hidden layer in the WELM are globally optimized to avoid the algorithm falling into local optimal solution and realize the classification of the NSL-KDD intrusion detection data set. The experimental results show that the proposed algorithm improves the recall rate of minority attacks and the accuracy of the overall classification, and reduces the false positive rate.

Key words: intrusion detection, unbalanced data set, weighted extreme learning machine, fruit fly optimization algorithm

中图分类号:

TP391.9

党建武, 谭凌. 改进果蝇算法优化加权极限学习机的入侵检测[J]. 系统仿真学报, 2021, 33(2): 331-338.

Dang Jianwu, Tan Ling. An Intrusion Detection Algorithm Based on IFOA and WELM[J]. Journal of System Simulation, 2021, 33(2): 331-338.

参考文献

[1] 康松林, 刘乐, 刘楚楚, 等. 多层极限学习机在入侵检测中的应用[J]. 计算机应用, 2015, 35(9): 2513-2518.
Kang Songlin, Liu Le, Liu Chuchu, et al.Intrusion Detection Based on Multiple Layer Extreme Learning Machine[J]. Journal of Computer Application, 2015, 35(9): 2513-2518.
[2] Akashdeep, Manzoor I, Kumar N. A Feature Reduced Intrusion Detection System Using ANN Classifier[J]. Expert Systems With Applications (S0957-4174), 2017, 88(12): 249-257.
[3] 姚潍, 王娟, 张胜利. 基于决策树与朴素贝叶斯分类的入侵检测模型[J]. 计算机应用, 2015, 35(10): 2883-2885.
Yao Wei, Wang Juan, Zhang Shengli.Intrusion Detection Model Based on Decision Tree and Naive-Bayes Classification[J]. Journal of Computer Application, 2015, 35(10): 2883-2885.
[4] Chitrakar R, Huang C H.Selection of Candidate Support Vectors in Incremental SVM for Network Intrusion Detection[J]. Computers and Security (S0167-4048), 2014, 45(3): 231-241.
[5] Gu J, Wang L H, Wang H W, et al.A Novel Approach to Intrusion Detection Using SVM Ensemble with Feature Augmentation[J]. Computers and Security (S0167-4048), 2019, 86(9): 53-62.
[6] 沈夏炯, 王龙, 韩道军. 人工蜂群优化的BP神经网络在入侵检测中的应用[J]. 计算机工程, 2016, 42(2): 190-194.
Shen Xiajiong, Wang Long, Han Daojun.Application of BP Neural Network Optimized by Artificial Bee Colony in Intrusion Detection[J]. Journal of Computer Engineering, 2016, 42(2): 190-194.
[7] 丁红卫, 万良, 邓烜堃. 改进的HS算法优化BP神经网络的入侵检测研究[J]. 计算机工程与科学, 2019, 41(1): 65-72.
Ding Hongwei, Wan Liang, Deng Xuankun.Optimizing Intrusion Detection of A Modified Harmony Search Algorithm[J]. Journal of Computer Engineering and Science, 2019, 41(1): 65-72.
[8] Zhu Y Z.Intrusion Detection Method Based on Improved BP Neural Network Research[J]. International Journal of Security and Its Applications (S1738-9976), 2016, 10(5): 193-202.
[9] Fung C J, Zhang J, Boutaba R.Effective Acquaintance Management Based on Bayesian Learning for Distributed Intrusion Detection Networks[J]. IEEE Transactions on Network and Service Management (S1932-4537), 2012, 9(3): 320-332.
[10] 马超. 基于ReliefF和改进乌鸦搜索优化的并行入侵检测方法[J]. 计算机应用研究, 2019, 36(10): 3063-3068.
Ma Chao.Network Intrusion Based on ReliefF and Improved Crow Search Optimization Parallel Method[J]. Journal of Application Research of Computers, 2019, 36(10): 3063-3068.
[11] Huang G B, Zhu Q Y, Siew C K.Extreme learning machine: Theory and application[J]. Neurocomputing (S0925-2312), 2006, 70(1/3): 489-501.
[12] 黄思慧, 陈万忠, 李晶. 基于PCA和ELM的网络入侵检测技术[J]. 吉林大学学报(信息科学版),2017, 35(5): 576-583.
Huang Sihui, Chen Wanzhong, Li Jing.Network Intrusion Detection Based on Extreme Learning Machine and Principal Component Analysis[J]. Journal of Jilin University (Information Science Edition), 2017, 35(5): 576-583.
[13] 王琳琳, 刘敬浩, 付晓梅. 基于极限学习机与改进K-means算法的入侵检测方法[J]. 计算机工程与科学, 2018, 40(8): 1398-1404.
Wang Linlin, Liu Jinghao, Fu Xiaomei.An Intrusion Detection Method Based on Extreme Learning Machine and Modified K-means[J]. Journal of Computer Engineering and Science, 2018, 40(8): 1398-1404.
[14] Al-Yaseen W L, Othman Z A, Nazri M Z A. Multi-level Hybrid Support Vector Machine and Extreme Learning Machine Based on Modified K-means for Intrusion Detection System[J]. Expert Systems with Applications (S0957-4174), 2017, 67(1): 296-303.
[15] Zong W W, Huang G B, Chen Y Q.Weighted Extreme Learning Machine for Imbalance Learning[J]. Neurocomputing (S0925-2312), 2013, 101(3): 229-242.
[16] Pan W T.A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as An Example[J]. Knowledge-based System (S0950-7051), 2012, 26(2): 69-74.
[17] Lv S X, Zeng Y R, Wang L.An Effctive Fruit Fly Optimization Algotithm with Hybrid Information Exchange and Its Applications[J]. International Journal of Machine Learning and Cybernetics (S1868-8071), 2018, 9(10): 1623-1648.
[18] Hu R, Wen S, Zeng Z.A Short-term Power Load Forecasting Model Based on the Generalized Regression Neural Network With Decreasing Step Fruit Fly Optimization Algorithm[J]. Neuroconputing (S0925-2312), 2017, 221(1): 24-31.
[19] 李少波, 赵辉, 赵成龙, 等. 果蝇优化算法研究综述[J]. 科学技术与工程, 2018, 18(1): 163-171.
Li Shaobo, Zhao Hui, Zhao Chenglong, et al.Review of Fruit Fly Optimization Algorithms[J]. Journal of Science Technology and Engineering, 2018, 18(1): 163-171.
[20] Shan D, Cao G H, Dong H J.LGMS-FOA: An Improved Fruit Fly Optimization Algorithm for Solving Optimization Problems[J]. Mathematical Problems in Engineering (S1563-5147), 2013(7): 1256-1271.
[21] Dhanabal L, Shantharajah S P.A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms[J]. International Journal of Advanced Research in Computer and Communication Engineering (S2319-5940), 2015, 4(6): 446-452.

改进果蝇算法优化加权极限学习机的入侵检测

An Intrusion Detection Algorithm Based on IFOA and WELM

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