基于自适应人工鱼群FCM的异常检测算法
|
摘要
异常检测算法在诸多领域都发挥着重要的作用.基于模糊C-均值(fuzzy C-means, FCM)的异常检测是其代表方法之一.FCM对初始值的选取很敏感,而且容易陷入局部极值.基于此的异常检测算法检测效果也不甚理想.因此,引入具有较强全局搜索能力的人工鱼群算法,对其加入自适应机制,自适应调整Visual取值范围,从而提高AFSA局部和全局寻优能力,减少算法迭代的次数.然后将其应用于FCM中,利用自适应人工鱼群算法得到的最优解进行FCM聚类分析,从而解决以上FCM存在的种种问题.最后,设计基于自适应人工鱼群FCM的异常检测算法,充分利用自适应人工鱼群的优势来提高异常检测算法的检测性能.实验表明:该算法在提高对数据的检测效率的基础上,检测性能也表现出了很好的水平,为解决异常检测模型中的检测率和虚警率相关问题提供了一种有效解决方案.
Anomaly detection algorithm has played a key role in many areas, and the anomaly detection based on fuzzy C-means(FCM) is one of its representative methods. Owing to the limits of FCM such as the local minimum and the sensitiveness of the selection of initial value, there is still a large room to improve the conditional FCM-based anomaly detection method. In this paper, we firstly propose an adaptive artificial fish-swarm algorithm(AAFSA), by introducing an adaptive mechanism implemented by adjusting the value range of parameter "Visual" to the artificial fish-swarm algorithm which has a strong global search ability, to improve local and global optimization abilities and reduce the times of iterations. The limits of FCM mentioned above therefore can be solved by using the optimal solution obtained from AAFSA. Then, an anomaly detection algorithm based on AAFSA-FCM is designed by making full use of advantages of AAFSA to enhance the detection performances of anomaly detection algorithm. The experimental results show that the algorithm improves the detection performance both efficiently and effectively, which provides an effective solution for solving the problems of detection rate and false alarm rate in anomaly detection models, and state-of-the-art results achieve the purpose of reducing computational costs.
Anomaly detection algorithm has played a key role in many areas, and the anomaly detection based on fuzzy C-means(FCM) is one of its representative methods. Owing to the limits of FCM such as the local minimum and the sensitiveness of the selection of initial value, there is still a large room to improve the conditional FCM-based anomaly detection method. In this paper, we firstly propose an adaptive artificial fish-swarm algorithm(AAFSA), by introducing an adaptive mechanism implemented by adjusting the value range of parameter "Visual" to the artificial fish-swarm algorithm which has a strong global search ability, to improve local and global optimization abilities and reduce the times of iterations. The limits of FCM mentioned above therefore can be solved by using the optimal solution obtained from AAFSA. Then, an anomaly detection algorithm based on AAFSA-FCM is designed by making full use of advantages of AAFSA to enhance the detection performances of anomaly detection algorithm. The experimental results show that the algorithm improves the detection performance both efficiently and effectively, which provides an effective solution for solving the problems of detection rate and false alarm rate in anomaly detection models, and state-of-the-art results achieve the purpose of reducing computational costs.
引文
[1]Mao Jiali,Jin Cheqing,Zhang Zhigang,et al.Anomaly detection for trajectory big data:Advancements and framework[J].Journal of Software,2017,28(1):17- 34 (in Chinese)(毛嘉莉,金澈清,章志刚,等.轨迹大数据异常检测:研究进展及系统框架[J].软件学报,2017,28(1):17- 34)
[2]Qian Yanyan,Li Yongzhong,Yu Xiya.Intrusion detection method based on multi-label and semi-supervised learning[J].Computer Science,2015,42(2):134- 136 (in Chinese)(钱燕燕,李永忠,余西亚.基于多标记与半监督学习的入侵检测方法研究[J].计算机科学,2015,42(2):134- 136)
[3]Yin Chunyong,Zhang Sun,Yin Zhichao,et al.Anomaly detection model based on data stream clustering[J/OL].Cluster Computing,2017 [2018-02-01].https://link.springer.com/articel/10.1007%2Fs10586-017-1066-2
[4]Qian Sen,Weng Guirong.Medical image segmentation based on FCM and level set algorithm[C] //Proc of the 7th IEEE Int Conf on Software Engineering and Service Science.Piscataway,NJ:IEEE,2017:225- 228
[5]Tang Chenghua,Liu Pengcheng,Tang Shensheng,et al.Anomaly intrusion behavior detection based on fuzzy clustering and features selection[J].Journal of Computer Research and Development,2015,52(3):718- 728 (in Chinese)(唐成华,刘鹏程,汤申生,等.基于特征选择的模糊聚类异常入侵行为检测[J].计算机研究与发展,2015,52(3):718- 728)
[6]Xue Xiao,Liu Yian,Kan Yuan,et al.A research of intrusion detection system based on FCM-GRNN clustering[J].Computer Simulation,2010,27(6):151- 154 (in Chinese)(薛潇,刘以安,阚媛,等.基于 FCM-GRNN聚类的入侵检测算法研究[J].计算机仿真,2010,27(6):151- 154)
[7]Zhang Min,Yu Jian.Fuzzy partitional clustering algorithms[J].Journal of Software,2004,15(6):858- 868 (in Chinese)(张敏,于剑.基于划分的模糊聚类算法[J].软件学报,2004,15(6):858- 868)
[8]Jansi S,Subashini P.Modified FCM using genetic algorithm for segmentation of MRI brain images[C] //Proc of the 2015 IEEE Int Conf on Computational Intelligence and Computing Research.Piscataway,NJ:IEEE,2015:150- 158
[9]Xiao Mansheng,Xiao Zhe,Wen Zhicheng,et al.Improved FCM clustering algorithm based on spatial correlation and membership smoothing[J].Journal of Electronics & Information Technology,2017,39(5):1123- 1129 (in Chinese)(肖满生,肖哲,文志诚,等.一种空间相关性与隶属度平滑的FCM改进算法[J].电子与信息学报,2017,39(5):1123- 1129)
[10]Chen Haipeng,Shen Xuanjing,Long Jianwu,et al.Fuzzy clustering algorithm for automatic identification of clusters[J].Acta Electronica Sinica,2017,45(3):687- 694 (in Chinese)(陈海鹏,申铉京,龙建武,等.自动确定聚类个数的模糊聚类算法[J].电子学报,2017,45(3):687- 694)
[11]Nalluri M S R,Saisujana T,Reddy K H,et al.An efficient feature selection using artificial fish swarm optimization and SVM classifier[C] //Proc of 2017 Int Conf on Networks & Advances in Computational Technologies.Piscataway,NJ:IEEE,2017:407- 411
[12]Manikandan R P S,Kalpana A M.Feature selection using fish swarm optimization in big data[J/OL].Cluster Computing,2017 [2018-02-01].https://link.springer.com/articel/10.1007%2Fs10586-017-1182-z
[13]Alobaidi A T S,Hussein S A.An improved artificial fish swarm algorithm to solve flexible job shop[C] //Proc of 2017 Annual Conf on New Trends in Information and Communications Technology Applications.Piscataway,NJ:IEEE,2017:7- 12
[14]Sengottuvelan P,Prasath N.BAFSA:Breeding artificial fish swarm algorithm for optimal cluster head selection in wireless sensor networks[J].Wireless Personal Communications,2017,94(4):1979- 1991
[15]Kumar K P,Saravanan B,Swarup K S.Day ahead scheduling of generation and storage sources in a microgrid using artificial fish swarm algorithm[C] //Proc of the 21st Int Conf on Century Energy Needs-Materials,Systems and Applications.Piscataway,NJ:IEEE,2016:Article Number 8052753
[16]Liu Rujuan,Jia Bin,Xin Yang.Network anomaly detection model based on information gain feature selection[J].Journal of Computer Applications,2016,36(Suppl2):49- 53 (in Chinese)(刘汝隽,贾斌,辛阳.基于信息增益特征选择的网络异常检测模型[J].计算机应用,2016,36(Suppl2):49- 53)
[17]Kumari V V,Varma P R K.A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering[C] //Proc of the 2017 Int Conf on IoT in Social,Mobile,Analytics and Cloud.Piscataway,NJ:IEEE,2017:481- 485
[18]Dubey Y K,Mushrif M M.FCM clustering algorithms for segmentation of brain MR images[J/OL].Advances in Fuzzy Systems,2016 [2018-02-01].https://leeexplore.leee.org/document/8554083
[19]Menon N,Ramakrishnan R.Brain tumor segmentation in MRI images using unsupervised artificial bee colony algorithm and FCM clustering[C] //Proc of the 2015 Int Conf on Communications and Signal Processing.Piscataway,NJ:IEEE,2015:6- 9
[20]Meng Anbo,Lu Haiming,Li Hailiang,et al.Electricity customer classification based on optimized FCM clustering by hybrid CSO[J].Power System Protection and Control,2015,43(20):150- 154 (in Chinese)(孟安波,卢海明,李海亮,等.纵横交叉算法优化FCM在电力客户分类中的应用[J].电力系统保护与控制,2015,43(20):150- 154)
[21]Gong Lingyun,Yang Jun,Zhang Leiqi,et al.Operational status recognition based on FCM and ANFIS for distribution network[J].Electric Power Automation Equipment,2016,36(4):85- 92 (in Chinese)(龚凌云,杨军,张磊琪,等.基于FCM和ANFIS的配电网运行状态识别策略[J].电力自动化设备,2016,36(4):85- 92)
[22]Xiang Shiming,Nie Feipeng,Zhang Changshui.Learning a Mahalanobis distance metric for data clustering and classification[J].Pattern Recognition,2008,41(12):3600- 3612
[23]Tang Zhengjun.Design and Implementation of Network Intrusion Detection System[M].Beijing:Electronic Industry Press,2002 (in Chinese)(唐正军.网络入侵检测系统的设计与实现[M].北京:电子工业出版社,2002)
[24]Li Xiaolei.A new intelligent optimization method:Artificial fish school algorithm[D].Hangzhou:Zhejiang University,2003 (in Chinese)(李晓磊.一种新型的智能优化方法:人工鱼群算法[D].杭州:浙江大学,2003)
[25]Wang Lianguo,Shi Qiuhong.Parameters analysis of artificial fish swarm algorithm[J].Computer Engineering,2010,36(24):169- 171 (in Chinese)(王联国,施秋红.人工鱼群算法的参数分析[J].计算机工程,2010,36(24):169- 171)
[26]Wang Hepeng,Wang Hongzhi,Li Jianzhong,et al.Algorithms for accurate decision tree generation on inconsistent data[J].Journal of Software,2017,28(11):2814- 2824 (in Chinese)(王鹤澎,王宏志,李建中,等.不一致数据上精确决策树生成算法[J].软件学报,2017,28(11):2814- 2824)
[2]Qian Yanyan,Li Yongzhong,Yu Xiya.Intrusion detection method based on multi-label and semi-supervised learning[J].Computer Science,2015,42(2):134- 136 (in Chinese)(钱燕燕,李永忠,余西亚.基于多标记与半监督学习的入侵检测方法研究[J].计算机科学,2015,42(2):134- 136)
[3]Yin Chunyong,Zhang Sun,Yin Zhichao,et al.Anomaly detection model based on data stream clustering[J/OL].Cluster Computing,2017 [2018-02-01].https://link.springer.com/articel/10.1007%2Fs10586-017-1066-2
[4]Qian Sen,Weng Guirong.Medical image segmentation based on FCM and level set algorithm[C] //Proc of the 7th IEEE Int Conf on Software Engineering and Service Science.Piscataway,NJ:IEEE,2017:225- 228
[5]Tang Chenghua,Liu Pengcheng,Tang Shensheng,et al.Anomaly intrusion behavior detection based on fuzzy clustering and features selection[J].Journal of Computer Research and Development,2015,52(3):718- 728 (in Chinese)(唐成华,刘鹏程,汤申生,等.基于特征选择的模糊聚类异常入侵行为检测[J].计算机研究与发展,2015,52(3):718- 728)
[6]Xue Xiao,Liu Yian,Kan Yuan,et al.A research of intrusion detection system based on FCM-GRNN clustering[J].Computer Simulation,2010,27(6):151- 154 (in Chinese)(薛潇,刘以安,阚媛,等.基于 FCM-GRNN聚类的入侵检测算法研究[J].计算机仿真,2010,27(6):151- 154)
[7]Zhang Min,Yu Jian.Fuzzy partitional clustering algorithms[J].Journal of Software,2004,15(6):858- 868 (in Chinese)(张敏,于剑.基于划分的模糊聚类算法[J].软件学报,2004,15(6):858- 868)
[8]Jansi S,Subashini P.Modified FCM using genetic algorithm for segmentation of MRI brain images[C] //Proc of the 2015 IEEE Int Conf on Computational Intelligence and Computing Research.Piscataway,NJ:IEEE,2015:150- 158
[9]Xiao Mansheng,Xiao Zhe,Wen Zhicheng,et al.Improved FCM clustering algorithm based on spatial correlation and membership smoothing[J].Journal of Electronics & Information Technology,2017,39(5):1123- 1129 (in Chinese)(肖满生,肖哲,文志诚,等.一种空间相关性与隶属度平滑的FCM改进算法[J].电子与信息学报,2017,39(5):1123- 1129)
[10]Chen Haipeng,Shen Xuanjing,Long Jianwu,et al.Fuzzy clustering algorithm for automatic identification of clusters[J].Acta Electronica Sinica,2017,45(3):687- 694 (in Chinese)(陈海鹏,申铉京,龙建武,等.自动确定聚类个数的模糊聚类算法[J].电子学报,2017,45(3):687- 694)
[11]Nalluri M S R,Saisujana T,Reddy K H,et al.An efficient feature selection using artificial fish swarm optimization and SVM classifier[C] //Proc of 2017 Int Conf on Networks & Advances in Computational Technologies.Piscataway,NJ:IEEE,2017:407- 411
[12]Manikandan R P S,Kalpana A M.Feature selection using fish swarm optimization in big data[J/OL].Cluster Computing,2017 [2018-02-01].https://link.springer.com/articel/10.1007%2Fs10586-017-1182-z
[13]Alobaidi A T S,Hussein S A.An improved artificial fish swarm algorithm to solve flexible job shop[C] //Proc of 2017 Annual Conf on New Trends in Information and Communications Technology Applications.Piscataway,NJ:IEEE,2017:7- 12
[14]Sengottuvelan P,Prasath N.BAFSA:Breeding artificial fish swarm algorithm for optimal cluster head selection in wireless sensor networks[J].Wireless Personal Communications,2017,94(4):1979- 1991
[15]Kumar K P,Saravanan B,Swarup K S.Day ahead scheduling of generation and storage sources in a microgrid using artificial fish swarm algorithm[C] //Proc of the 21st Int Conf on Century Energy Needs-Materials,Systems and Applications.Piscataway,NJ:IEEE,2016:Article Number 8052753
[16]Liu Rujuan,Jia Bin,Xin Yang.Network anomaly detection model based on information gain feature selection[J].Journal of Computer Applications,2016,36(Suppl2):49- 53 (in Chinese)(刘汝隽,贾斌,辛阳.基于信息增益特征选择的网络异常检测模型[J].计算机应用,2016,36(Suppl2):49- 53)
[17]Kumari V V,Varma P R K.A semi-supervised intrusion detection system using active learning SVM and fuzzy c-means clustering[C] //Proc of the 2017 Int Conf on IoT in Social,Mobile,Analytics and Cloud.Piscataway,NJ:IEEE,2017:481- 485
[18]Dubey Y K,Mushrif M M.FCM clustering algorithms for segmentation of brain MR images[J/OL].Advances in Fuzzy Systems,2016 [2018-02-01].https://leeexplore.leee.org/document/8554083
[19]Menon N,Ramakrishnan R.Brain tumor segmentation in MRI images using unsupervised artificial bee colony algorithm and FCM clustering[C] //Proc of the 2015 Int Conf on Communications and Signal Processing.Piscataway,NJ:IEEE,2015:6- 9
[20]Meng Anbo,Lu Haiming,Li Hailiang,et al.Electricity customer classification based on optimized FCM clustering by hybrid CSO[J].Power System Protection and Control,2015,43(20):150- 154 (in Chinese)(孟安波,卢海明,李海亮,等.纵横交叉算法优化FCM在电力客户分类中的应用[J].电力系统保护与控制,2015,43(20):150- 154)
[21]Gong Lingyun,Yang Jun,Zhang Leiqi,et al.Operational status recognition based on FCM and ANFIS for distribution network[J].Electric Power Automation Equipment,2016,36(4):85- 92 (in Chinese)(龚凌云,杨军,张磊琪,等.基于FCM和ANFIS的配电网运行状态识别策略[J].电力自动化设备,2016,36(4):85- 92)
[22]Xiang Shiming,Nie Feipeng,Zhang Changshui.Learning a Mahalanobis distance metric for data clustering and classification[J].Pattern Recognition,2008,41(12):3600- 3612
[23]Tang Zhengjun.Design and Implementation of Network Intrusion Detection System[M].Beijing:Electronic Industry Press,2002 (in Chinese)(唐正军.网络入侵检测系统的设计与实现[M].北京:电子工业出版社,2002)
[24]Li Xiaolei.A new intelligent optimization method:Artificial fish school algorithm[D].Hangzhou:Zhejiang University,2003 (in Chinese)(李晓磊.一种新型的智能优化方法:人工鱼群算法[D].杭州:浙江大学,2003)
[25]Wang Lianguo,Shi Qiuhong.Parameters analysis of artificial fish swarm algorithm[J].Computer Engineering,2010,36(24):169- 171 (in Chinese)(王联国,施秋红.人工鱼群算法的参数分析[J].计算机工程,2010,36(24):169- 171)
[26]Wang Hepeng,Wang Hongzhi,Li Jianzhong,et al.Algorithms for accurate decision tree generation on inconsistent data[J].Journal of Software,2017,28(11):2814- 2824 (in Chinese)(王鹤澎,王宏志,李建中,等.不一致数据上精确决策树生成算法[J].软件学报,2017,28(11):2814- 2824)
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |