A Signal Analysis of Network Traffic Anomalies Paul Barford with Jeffery Kline, David Plonka, Amos Ron University of Wisconsin – Madison Summer, 2002

2 Motivation Traffic anomalies are a fact of life in computer networks –Outages, attacks, etc… Anomaly detection and identification is challenging –Operators typically monitor by eye using SNMP or IP flows Obviously, this does not scale! –Simple thresholding is ineffective –Some anomalies are obvious, other are not Characteristics of anomalous behavior in IP traffic are not well understood –Do same types of anomalies have same characteristics? –Can characteristics be effectively used in detection systems?

3 Introduction Objective: Improve our understanding network traffic anomalies Approach: Wavelet analysis of data set that includes IP flow data, SNMP data and a catalog of observed anomalies Method: Integrated Measurement Analysis Platform for Internet Traffic (IMAPIT) Results: We demonstrate how anomalies can be exposed using wavelets and develop new method for exposing short-lived events

4 Related Work Network traffic characterization –Eg. Caceres89, Leland93, Paxson97, Zhang01 Focus on typical behavior –Abry98 use wavelets to analyze LRD traffic Fault and anomaly detection techniques –Eg. Feather93, Brutlag00 Focus on thresholds and time series models –Eg. Paxson99 Rule based tool for intrusion detection –Eg. Moore01 Backscatter technique can be used to identify DoS attacks –Eg. Huang01 Wavelet-based approach to detecting network performance problems

5 Simple Network Management Protocol SNMP is the standard protocol for monitoring/managing networked systems SNMP defines a set of MIB (management information base) data exported from routers –RFC2863 We sample High Capacity Interface using MRTG (Multi-Router Traffic Grapher) at 5 minute intervals –Archive byte and packet traffic in each direction –64-bit counters on each of 15 WAN links SNMP count precision is yet to be determined…

6 IP Flows An IP Flow is defined as a unidirectional series of packets between source/dest IP/port pair over a period of time –Exported by Lightweight Flow Accounting Protocol (LFAP) enabled routers (Cisco’s NetFlow, Juniper cflowd flow export) We use FlowScan [Plonka00] to collect and post-process IP flow data collected at 5 minute intervals –Combines flow collection engine, database, visulaization tool –Provides a near real-time visualization of network traffic –Breaks down traffic into well known service or application {SRC_IP/Port,DST_IP/Port,Pkts,Bytes,Start/End Time,TCP Flags,IP Prot …}

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8 Our Approach to Data Gathering Consider anomalies in IP flow and SNMP data –Collected at UW border router (Juniper M10) –Archive of ~6 months worth of data (packets, bytes, flows) –Includes catalog of anomalies (after-the-fact analysis) Group observed anomalies into four categories –Network anomalies (41) Steep drop offs in service followed by quick return to normal behavior –Flash crowd anomalies (4) Steep increase in service followed by slow return to normal behavior –Attack anomalies (46) Steep increase in flows in one direction followed by quick return to normal behavior –Measurement anomalies (18) Short-lived anomalies which are not network anomalies or attacks

9 Our Approach to Analysis Wavelets provide a means for describing time series data that considers both frequency and time –Particularly useful for characterizing data with sharp spikes and discontinuities More robust than Fourier analysis which only shows what frequencies exist in a signal –Tricky to determine which wavelets provide best resolution of signals in data We use tools developed at UW which together make up IMAPIT –FlowScan software –The IDR Framenet software

10 Our Wavelet System After evaluating different candidates we selected a wavelet system called Pseudo Splines(4,1) Type 2. –A framelet system developed by Daubechies et al. ‘00 –Very good frequency localization properties Three output signals are extracted from input –Low Frequency (L): synthesis of all wavelet coefficients from level 9 and up –Mid Frequency (M): synthesis of wavelet coefficients 6, 7, 8 –High Frequency (H): synthesis of wavelet coefficients 1 to 5 Thresholding (set to zero all coefficients whose absolute value is below a threshold) is used on these coefficients

11 Ambient IP Flow Traffic

12 Ambient SNMP Traffic

13 Byte Traffic for Flash Crowd

14 Average Packet Size for Flash Crowd

15 Flow Traffic During DoS Attacks

16 Byte Traffic During Measurement Anomalies

17 Anomaly Detection via Deviation Score We develop an automated means for identifying short- lived anomalies based on variability in H and M signals 1.Compute local variability (using specified window) of H and M parts of signal 2.Combine local variability of H and M signals (using a weighted sum) and normalize by total variability to get deviation score V 3.Apply threshold to V then measure peaks Our analysis shows that V peaks over 2.0 indicate short-lived anomalies with high confidence –We threshold at V = 1.25 and set window size to ~3 hours

18 Deviation Score for Three Anomalies

19 Deviation Score for Network Outage

20 Anomalies in Aggregate Signals

21 Hidden Anomalies in Low Frequency

22 Deviation Score Evaluation How effective is deviation score at detecting anomalies? –Compare versus set of 39 anomalies Set is unlikely to be complete so we don’t treat false-positives –Compare versus Holt-Winters Forecasting Sophisticated time series technique Requires some configuration Holt-Winters reported many more positives and sometimes oscillated between values Total Candidate Anomalies Candidates detected by Deviation Score Candidates detected by Holt-Winters

23 Conclusion and Next Steps We present an evaluation of signal characteristics of network traffic anomalies –Using IP flow and SNMP data collected at UW border router 106 anomalies have been grouped into four categories –IMAPIT developed to apply wavelet analysis to data –Deviation score developed to automate anomaly detection Results –Characteristics of anomalies exposed using different filters and data –Deviation score is effective detection method Future –Development of anomaly classification methods –Application of results in (distributed) detection systems