基于LOF的电力数据网业务流量异常检测
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- 英文论文题名：LOF based TrafficAnomaly Detection Algorithm of Electric Power Data Network
- 论文作者：应斐昊 ; 邢宁哲 ; 纪雨彤 ; 纪晨晨 ; 李文璟
- 年：2016
- 论文关键词：电力数据网 ; 流量异常检测 ; KTLAD
- 英文论文关键词：Electric Power Data Network ; TrafficAnomaly Detection ; KTLAD
- 会议召开时间：2016-06-24
- 会议录名称：2016年全国通信软件学术会议程序册与交流文集
- 语种：中文
- 分类号：TM73;TM76
- 学会代码：ZGTH
- 会议名称：2016年全国通信软件学术会议
- 会议地点：中国陕西西安
- 主办单位：中国通信学会
- 学会名称：中国通信学会
- 页数：6
- 文件大小：807k
- 原文格式：O
- 会议级别：全国
摘要
针对电力数据网对流量异常检测的时效性要求,提出一种改进的LOF异常检测方法——KTLAD。该方法基于密度进行检测,计算每个流量包与附近流量包的分隔程度,无需预先设置流量的具体异常状态,相对传统方法具有很高的灵活性。仿真结果验证了KTLAD在电力数据网中业务流量异常检测中的可行性,并且有效地降低了时间成本。
Due to the efficiency requirements of traffic anomaly detection in electric power data network,an improved anomaly detection algorithm named KTLAD based on LOF is proposed.Based on density detection,the algorithm calculates the separating level of each traffic package with nearby ones,without pre-set specific abnormal state of traffic.Comparing to the traditional algorithms,the proposed algorithm is more flexible.Simulation results show that the KTLAD is feasible and effective in traffic anomaly detection in electric power data network.
Due to the efficiency requirements of traffic anomaly detection in electric power data network,an improved anomaly detection algorithm named KTLAD based on LOF is proposed.Based on density detection,the algorithm calculates the separating level of each traffic package with nearby ones,without pre-set specific abnormal state of traffic.Comparing to the traditional algorithms,the proposed algorithm is more flexible.Simulation results show that the KTLAD is feasible and effective in traffic anomaly detection in electric power data network.
引文
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[5]Ming Zhang,Boyi Xu,Jie Gong.An Anomaly Detection Model Based o.n One-Class SVM to Detect Network Intrusions[C].//2015 11th International Conference on Mobile Ad-hoc and Sensor Networks(MSN).Shenzhen,2015:102-107.
[6]X Tang.The Stream Detection Based on Local Outlier Factor[J].《Journal of Information&Computational Science》,2015,12(17):6361-6369.
[7]Tarassenko L,Hayton P,Brady M.Novelty detection for the identification of masses in mammograms[C]//Fourth International Conference on Artificial Neural Networks,London,l 995:442-447.
[2]王娟,靳京,钱伟中.基于小波分解的群落流量异常检测[J].电子测量与仪器学报,2010,24(4):365-370.Wang Juan,Jin Jing,Qian Weizhong.Community traffic anomaly detection using wavelet analysis[J].Journal of Electronic Measurement and Instrument,2010,24(4):265-370.
[3]王辉,陈泓予,刘淑芬.基于改进朴素贝叶斯算法的入侵检测系统[J].《计算机科学》,2014,41(4):111-115.
[4]穆祥昆,王劲松,薛羽丰等.基于活跃熵的网络异常流量检测方法[J].通信学报,2013,34(z2):51-57.Mu Xiangkun,Wang Jinsong,Xue Yufeng,et al.Abnormal network traffic detection approach based on alive entropy[J].Journal on Communications,2013,34(z2):51-57.
[5]Ming Zhang,Boyi Xu,Jie Gong.An Anomaly Detection Model Based o.n One-Class SVM to Detect Network Intrusions[C].//2015 11th International Conference on Mobile Ad-hoc and Sensor Networks(MSN).Shenzhen,2015:102-107.
[6]X Tang.The Stream Detection Based on Local Outlier Factor[J].《Journal of Information&Computational Science》,2015,12(17):6361-6369.
[7]Tarassenko L,Hayton P,Brady M.Novelty detection for the identification of masses in mammograms[C]//Fourth International Conference on Artificial Neural Networks,London,l 995:442-447.