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Characteristics of Network Traffic Flow Anomalies

Characteristics of Network Traffic Flow Anomalies. Paul Barford and David Plonka University of Wisconsin – Madison SIGCOMM IMW, 2001. Motivation. Traffic anomalies are a fact of life in computer networks Anomaly detection and identification is challenging

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Characteristics of Network Traffic Flow Anomalies

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  1. Characteristics of Network Traffic Flow Anomalies Paul Barford and David Plonka University of Wisconsin – Madison SIGCOMM IMW, 2001

  2. Motivation • Traffic anomalies are a fact of life in computer networks • Anomaly detection and identification is challenging • Operators typically monitor by eye using SNMP or IP flows • Simple thresholding is ineffective • Some anomalies are obvious, other are not • Characteristics of anomalous behavior in IP flows have not been established • Do same types of anomalies have same characteristics? • Can characteristics be effectively used in detection systems? IMW 2001

  3. Related Work • Network traffic characterization • Eg. Caceres89, Leland93, Paxson97, Zhang01 • Focus on typical behavior • 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 • No work which identifies anomaly characteristics IMW 2001

  4. Our Approach to Data Gathering • Consider anomalies in IP flow data • Collected at UW border router - 5 minute intervals • Archive of two years worth of data (packets, bytes, flows) • Includes identification of anomalies (after-the-fact analysis) • Group anomalies into three categories • Network operation anomalies • Steep drop offs in service followed by quick return to normal behavior • Flash crowd anomalies • Steep increase in service followed by slow return to normal behavior • Network abuse anomalies • Steep increase in flows in one direction followed by quick return to normal behavior IMW 2001

  5. 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) • We use FlowScan [Plonka00] to collect and process Netflow data • 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 …} IMW 2001

  6. Characteristics of “Normal” traffic IMW 2001

  7. Our Approach to Analysis • Analyze examples of each type of anomaly via statistics, time series and wavelets (our initial focus) • Wavelets provide a means for describing time series data that considers both frequency and scale • 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 Wavelet IDR center • First step: Identify which filters isolate anomalies IMW 2001

  8. First Look at Analysis of “Normal” Traffic • Wavelets easily localize familiar daily/weekly signals IMW 2001

  9. First Look Analysis of Attacks • DoS: sharp increase in flows and/or packets in one direction • Linear splines seem to be a good filter to distinguish DoS attacks IMW 2001

  10. Characteristics of Flash Crowds • Sharp increase in packets/bytes/flows followed by slow return to normal behavior eg. Linux releases • Leading edge not significantly different from DoS signal so next step is to look within the spikes IMW 2001

  11. Characteristics of Network Anomalies • Typically a steep drop off in packets/bytes/flows followed a short time later by restoration IMW 2001

  12. Conclusion and Next Steps • Project to characterize network traffic flow anomalies • Based on flow data collected at UW border router • Anomalies have been grouped into three categories • Analysis approach: statistical, time series, wavelet • Initial results • Good indications that we can isolate signals • Future • Continue analysis of anomaly data • Analysis of data from other sites • Application of results in (distributed) detection systems IMW 2001

  13. Acknowledgements • Somesh Jha • Jeff Kline • Amos Ron IMW 2001

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