インターネットと運用技術シンポジウム 2016 Internet and Operation Technology Symposium 2016 IOTS /12/1 syslog 1,2,a) 3,b) syslog syslog syslog Interop Tokyo Show
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1 syslog 1,2,a) 3,b) syslog syslog syslog Interop Tokyo ShowNet syslog Proposal of the anomaly detection method analyzing syslog data using Bollinger Bands algorithm on event network Hiroshi Abe 1,2,a) Mikifumi Shikida 3,b) / 1 IIJ 2 3 a) abe@iij.ad.jp/h-abe@jaist.ac.jp b) shikida.mikifumi@kochi-tech.ac.jp / / syslog syslog 57
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2 1 VMware vrealize LogInsight syslog Interop Tokyo[1] ShowNet[2] syslog 1.2 ShowNet ShowNet Interop Tokyo 2 ShowNet ShowNet ShowNet syslog ShowNet 1.3 ShowNet syslog ShowNet 1 syslog 1 VMware vrealize LogInsight [3](LogInsight) syslog LogInsight, (OSPF down/bgp down/storm detection ) ( ) ShowNet debug info ShowNet syslog 1.4 2, 3 4, Holt-Winters [4] ShowNet Jon Kleinberg [5] Kleinberg 58
![syslog ChangeFinder[6] ChangeFinder. Google word2vec[7]. syslog 3. 3.1 ShowNet syslog ShowNet syslog [8] 3.2 1 1980 John Bollinger 2 2 ( ) 68.](/docs-images/102/153821503/images/3-0.jpg "26% 2 ( ) 95.44% 3 ( ) 99.73% 95.44% 2 +2-2 + 2 ( ) UpperLimit() - 2 ( ) LowerLimit() () x = 1 n n 1 x i i=0 ( ) σ = 1 n 1 (x i x) 2 n i=0 59")
3 syslog ChangeFinder[6] ChangeFinder. Google word2vec[7]. syslog ShowNet syslog ShowNet syslog [8] John Bollinger 2 2 ( ) 68.26% 2 ( ) 95.44% 3 ( ) 99.73% 95.44% ( ) UpperLimit() - 2 ( ) LowerLimit() () x = 1 n n 1 x i i=0 ( ) σ = 1 n 1 (x i x) 2 n i=0 59
4 OS CentOS Python CPU Intel(R) Xeon(R) CPU E GHz 128GB { n 1 = 1 n 2 n x 2 i i=0 ( n 1 ) 2 } x i i=0 ( ) 2 UpperLimit, LowerLimit 3.3 syslog syslog 2 ( ) syslog ( 2 ) 95.44% UpperLimit LowerLimit 4.56% 4.56% ShowNet ShowNet Python syslog 6.4GB 4, syslog. syslog [9] Mmm dd hh:mm:ss IP 1 import pandas as pd 2 df = pd. read_csv (./ syslog. log, delim_whitespace =True,...) 3 count = df. groupby (pd. TimeGrouper ( 1 Min )). count () 4 mean = count. rolling ( window =60). mean () 5 std = count. rolling ( window =60). std () 6 std_plus = std. apply ( lambda x: x * 2) 7 std_ minus = std. apply ( lambda x: x * -2) 8 upper_ limit = mean. add ( std_plus ) 9 lower_limit = mean. add ( std_minus ) 3 2 csv csv Python pandas[10] csv pandas DataFrame DataFrame pandas 1 1 DataFrame DataFrame ( : mean : std ) 1 / 1 1 (0 23 ) 1 / ) pandas 2) (csv ) 3) DataFrame 1 4) mean 5) std 6) 2 (+2 ) 60
5 4 1 2 Level Low Middle High / % 5/28 181, % 5/29 552, % 5/30 821, % 5/31 617, % 6/1 917, % 6/2 1,949, % 6/3 1,771, % 6/4 2,108, % 6/5 3,177, % 6/6 3,297, % 6/7 2,702, % 6/8 3,186, % 6/9 12,769, % 6/10 9,446, % 43,500, % 7) -2 (-2 ) 8) (UpperLimit) 9) (LowerLimit) 5/27 4 count 1 Upper- Limit LowerLimit UpperLimit LowerLimit +2 UpperLimit UpperLimit % UpperLimit syslog DoS(Denial of Service). +2 ( 2) Low, Middle, High ShowNet syslog 3 3 ( 1 ) ( 2 ) ( 3 ) ( 4 ) ( 5 ) 5 syslog (1 :60 ) 1 (86400 ) (86400/60=1440) 5/ syslog 5/27 syslog. ShowNet syslog. 6/ ShowNet UpperLimit UpperLimit ShowNet 5.50% 94.5% UpperLimit ShowNet (Hotstage) 61
6 4 Low Middle High 5/ / / / / / / / / / / / / / / % 46.93% 11.31% 5 6/6 3 Hotstage 5/27 6/3, 6/4 6/7, 6/8 6/10 Hotstage syslog ShowNet ,200 3% 6% Low 41.76%, Middle 46.93%, High 11.31% Low Middle 88% 5.3 ShowNet High syslog /6 6/6 18 ( 5) /8 SNMP Get request is recieved. :... SNMP Get response is sent. :... SNMP Get request response 18:10-18:16 ShowNet OID SNMP Get 6/6 18:10-18:16 SNMP SNMP ACL SNMP SNMP Daemon /8 6/9 6/9 3 6/1 6/ /9 1,200 6/ /10 1 9,000 6/8 23 6/6 SNMP SNMP 62
7 7 6/9 94.5% UpperLimit 8 6/10 UpperLimit 6/8 High 6/9( 7) 6/ % /10 8 6/10 8 ShowNet NFV(Network Functional Virtualization) BGP(Border Gateway Protocol) 5 High High ShowNet syslog % Low Middle 88% High High UpperLimit 6.3 syslog 6.4 ShowNet
8 2-3 ShowNet ShowNet syslog ShowNet syslog % UpperLimit. UpperLimit LowerLimit syslog syslog [1] Interop Tokyo, [2] ShowNet, [3] VMware vrealize Log Insight, [4] Kalekar, Prajakta S.: Time series forecasting using holtwinters exponential smoothing. Kanwal Rekhi School of Information Technology (2004): [5] Kleinberg,J.: Bursty and Hierarchical Structure in Streams,Proc,8th SIGKDD pp.91101,2002. [6] J. Takeuchi and K. Yamanishi : A Unifying Framework for Detecting Outliers and Change Points from Time Series, IEEE transactions on Knowledge and Data Engineering, vol.18, no.4, pp , [7] word2vec, [8] Bollinger, J. : Bollinger on Bollinger Bands. McGraw Hill, 2002 [9] Gerhards, R : RFC 5424,The Syslog Protocol, March 2009, [10] pandas, Python Data Analysis Library: ShowNet. ShowNet / 64
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