Review of Anomaly Detection Based on Log Analysis

[1]Yuan D, Park S, Huang P, Liu Y, Lee MM, Tang X, Zhou Y, Savage S. Be conservative: enhancing failure diagnosis with proactive logging. In: Proc. of the 10th Symp. on Operating Systems Design and Implementation (OSDI). 2012. 293~306.YuanDParkSHuangPLiuYLeeMMTangXZhouYSavageSBe conservative: enhancing failure diagnosis with proactive logging. In:Proc. of the 10th Symp. on Operating Systems Design and Implementation (OSDI)2012293306Search in Google Scholar

[2]Yuan D, Park S, Zhou Y. Characterizing logging practices in open-source software. In: Proc. of the 2012 Int’l Conf. on Software Engineering. 2012. 102~112. [doi: 10.1109/ICSE. 2012.6227202].YuanDParkSZhouYCharacterizing logging practices in open-source software. In:Proc. of the 2012 Int’l Conf. on Software Engineering2012102112. [doi: 10.1109/ICSE. 2012.6227202]Search in Google Scholar

[3]Peng Dong. Intelligent operation and maintenance: building a large-scale distributed AIOps system from zero. Electronic Industry Press, 2018. 7 ISBN 978-7-121-34663-7 p198-p199.PengDongIntelligent operation and maintenance: building a large-scale distributed AIOps system from zeroElectronic Industry Press20187978-7-121-34663-7p198p199Search in Google Scholar

[4]Varun Chandola, Arindam Banerjee, Vipin Kumar. Anomaly Detection: A Survey[J]. Acm Computing Surveys, 2009, 41(3).VarunChandolaArindamBanerjeeVipinKumarAnomaly Detection: A Survey[J]Acm Computing Surveys200941310.1145/1541880.1541882Search in Google Scholar

[5]Davis J J, Clark A J. Data preprocessing for anomaly based network intrusion detection: A review[J]. Computers & Security, 2011, 30(6-7):353-375.DavisJ JClarkA JData preprocessing for anomaly based network intrusion detection: A review[J]Computers & Security2011306-735337510.1016/j.cose.2011.05.008Search in Google Scholar

[6]Q. Lin, H. Zhang, J. Lou, Y. Zhang and X. Chen, “Log Clustering Based Problem Identification for Online Service Systems,” 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C), Austin, TX, 2016, pp. 102-111.Q.LinH.ZhangJ.LouY.Zhang and X.Chen, “Log Clustering Based Problem Identification for Online Service Systems,”2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C)Austin, TX201610211110.1145/2889160.2889232Search in Google Scholar

[7]Pecchia A, Cotroneo D, Kalbarczyk Z, et al. Improving Log-based Field Failure Data Analysis of multi-node computing systems[C]. IEEE, 2011.PecchiaACotroneoDKalbarczykZImproving Log-based Field Failure Data Analysis of multi-node computing systems[C]IEEE201110.1109/DSN.2011.5958210Search in Google Scholar

[8]Tambe R, Karabatis G, Janeja V P. Context aware discovery in web data through anomaly detection[J]. International Journal of Web Engineering and Technology, 2015, 10(1):3.TambeRKarabatisGJanejaV PContext aware discovery in web data through anomaly detection[J]International Journal of Web Engineering and Technology2015101310.1504/IJWET.2015.069348Search in Google Scholar

[9]Wang Xiaodong, Zhao Yining, Xiao Haili, Chi Xuebin, Wang Xiaoning. Detection method of abnormal log flow pattern in multi-node system [J/OL]. Journal of Software: 1-15 [2019-12-24].WangXiaodongZhaoYiningXiaoHailiChiXuebinWangXiaoningDetection method of abnormal log flow pattern in multi-node system [J/OL]Journal of Software115[2019-12-24]Search in Google Scholar

[10]Wang Zhiyuan, Ren Chongguang, Chen Rong, Qin Li. Anomaly detection technology based on log template[J]. Intelligent Computers and Applications, 2018, 8(05): 17-20+24.WangZhiyuanRenChongguangChenRongQinLiAnomaly detection technology based on log template[J]Intelligent Computers and Applications2018805172024Search in Google Scholar

[11]Son S, Gil MS, Moon YS. [IEEE 2017 IEEE International Conference on Big Data and Smart Computing (BigComp)-Jeju Island, South Korea (2017.2.13-2017.2.16)] 2017 IEEE International Conference on Big Data and Smart Computing (BigComp)-Anomaly detection for big log data using a Hadoop ecosystem[J]. 2017:377-380.SonSGilMSMoonYS[IEEE 2017 IEEE International Conference on Big Data and Smart Computing (BigComp)-Jeju Island, South Korea (2017.2.13-2017.2.16)] 2017 IEEE International Conference on Big Data and Smart Computing (BigComp)-Anomaly detection for big log data using a Hadoop ecosystem[J]2017377380Search in Google Scholar

[12]Fu, Q., Lou, JG, Wang, Y., & Li, J. (2009). Execution anomaly detection in distributed systems through unstructured log analysis. In Proceedings of the 2009 ninth IEEE international conference on data mining, ICDM ’09, (pp. 149–158). Washington, DC: IEEE Computer Society. doi:10.1109/ICDM.2009.60.FuQ.LouJGWangY., & LiJ.2009Execution anomaly detection in distributed systems through unstructured log analysisIn Proceedings of the 2009 ninth IEEE international conference on data mining, ICDM ’09, (pp.149158Washington, DCIEEE Computer Society10.1109/ICDM.2009.60Open DOISearch in Google Scholar

[13]Xu W, et al. Large-scale system problems detection by mining console logs[J]. Proceedings of the Acm Sigops Symposium on Operating Systems Principles Big Sky Mt, 2013:2009.XuWLarge-scale system problems detection by mining console logs[J]Proceedings of the Acm Sigops Symposium on Operating Systems Principles Big Sky Mt2013200910.1145/1629575.1629587Search in Google Scholar

[14]Ilenia Fronza, Alberto Sillitti, Giancarlo Succi, Mikko Terho, Jelena Vlasenko. Failure prediction based on log files using Random Indexing and Support Vector Machines[J]. Journal of Systems and Software, 2013, 86(1):2-11.IleniaFronzaAlbertoSillittiGiancarloSucciMikkoTerhoJelenaVlasenkoFailure prediction based on log files using Random Indexing and Support Vector Machines[J]Journal of Systems and Software201386121110.1016/j.jss.2012.06.025Search in Google Scholar

[15]Peng W, Li T, Ma S. Mining logs files for data-driven system management. ACM SIGKDD Explorations Newsletter, 2005, 7(1):44-51.PengWLiTMaSMining logs files for data-driven system managementACM SIGKDD Explorations Newsletter200571445110.1145/1089815.1089822Search in Google Scholar

[16]Zhang Luqing. Web attack data mining algorithm based on outlier anomaly[J]. Ship Electronic Engineering, 2018, 38(09): 105-110.ZhangLuqingWeb attack data mining algorithm based on outlier anomaly[J]Ship Electronic Engineering20183809105110Search in Google Scholar

[17]Breier J, Jana Branišová. A Dynamic Rule Creation Based Anomaly Detection Method for Identifying Security Breaches in Log Records[J]. Wireless Personal Communications, 2015, 94(3):1-15.BreierJJanaBranišováA Dynamic Rule Creation Based Anomaly Detection Method for Identifying Security Breaches in Log Records[J]Wireless Personal Communications2015943115Search in Google Scholar

[18]Zhang Zhongping, Liang Yongxin. Algorithm for mining outliers in flow data based on anti-k nearest neighbors[J]. Computer Engineering, 2009, 35(12): 11-13.ZhangZhongpingLiangYongxinAlgorithm for mining outliers in flow data based on anti-k nearest neighbors[J]Computer Engineering200935121113Search in Google Scholar

[19]Grace, L., Maheswari, V., & Nagamalai, D. (2011). Web log data analysis and mining. In N. Meghanathan, B. Kaushik, & D. Nagamalai (Eds.), Advanced computing, communications in computer and information science (Vol. 133, pp. 459–469). Berlin: Springer.GraceL.MaheswariV., & NagamalaiD.2011Web log data analysis and mining. InN.MeghanathanB.Kaushik, & D.Nagamalai(Eds.)Advanced computing, communications in computer and information science133459469BerlinSpringerSearch in Google Scholar

[20]Liang Bao, Qian Li, Peiyao Lu, Jie Lu, Tongxiao Ruan, Ke Zhang. (2018). Execution anomaly detection in large-scale systems through console log analysis. The Journal of Systems & Software 143 (2018) 172–186.LiangBaoQianLiPeiyaoLuJieLuTongxiaoRuanKeZhang2018Execution anomaly detection in large-scale systems through console log analysisThe Journal of Systems & Software143201817218610.1016/j.jss.2018.05.016Search in Google Scholar

[21]Liu F T, Ting K M, Zhou Z H. Isolation-Based Anomaly Detection[J]. ACM Transactions on Knowledge Discovery from Data, 2012, 6(1):1-39.LiuF TTingK MZhouZ HIsolation-Based Anomaly Detection[J]ACM Transactions on Knowledge Discovery from Data20126113910.1145/2133360.2133363Search in Google Scholar

[22]Wang Jinghua, Zhao Xinxiang, Zhang Guoyan, Liu Jianyin. NLOF: A new density-based local outlier detection algorithm [J]. Computer Science, 2013, 40(08): 181-185.WangJinghuaZhaoXinxiangZhangGuoyanLiuJianyinNLOF: A new density-based local outlier detection algorithm [J]Computer Science20134008181185Search in Google Scholar

[23]Li Shaobo, Meng Wei, Wei Jinglei. Density-based abnormal data detection algorithm GSWCLOF[J]. Computer Engineering and Applications, 2016, 52(19): 7-11.LiShaoboMengWeiWeiJingleiDensity-based abnormal data detection algorithm GSWCLOF[J]Computer Engineering and Applications20165219711Search in Google Scholar

[24]Wang Qian, Liu Shuzhi. Improvement of local outlier data mining method based on density [J]. Application Research of Computers, 2014, 31(06): 1693-1696+1701.WangQianLiuShuzhiImprovement of local outlier data mining method based on density [J]Application Research of Computers2014310616931696+1701Search in Google Scholar

[25]Pukelsheim F. The Three Sigma Rule[J]. The American Statistician, 1994, 48(2):88-91.PukelsheimFThe Three Sigma Rule[J]The American Statistician19944828891Search in Google Scholar