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Southeast University Dept. of Cs. & Eng AsiaFI School Wang Yang Southeast University August 2008 FAME : Factor Analysis Based Metrics Exploring Algorithm

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Southeast University Dept. of Cs. & Eng. AsiaFI School Outline Introduction Basic of FA FAME algorithm Experiments Conclusion and Future work

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Southeast University Dept. of Cs. & Eng. AsiaFI School Introduction Basics of Metrics Basic of network behavior research we need different metrics to describe different network research objects behavior. Example the Object of network behavior research different levels: link, packets, flows, sessions

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Southeast University Dept. of Cs. & Eng. AsiaFI School Introduction Basics of Metrics Atomic metrics Describes the objects direct property that cannot be further decomposition Derivative metrics Derived from the atomic metric through limited elementary operations and can reflect the characteristics of the object.

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Southeast University Dept. of Cs. & Eng. AsiaFI School Introduction Atomic metrics exploring method Rules: measurability, repeatability of measuring process Research instinct Enumerate every possibility IETF IPPM WG: connectivity; one-way delay; one-way packet loss rate

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Southeast University Dept. of Cs. & Eng. AsiaFI School Introduction Derivative metrics exploring method Enumerate different operations on atomic metrics Andrew Moore : mean, variance, FFT

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Southeast University Dept. of Cs. & Eng. AsiaFI School Introduction Shortcoming Atomic metrics: Reflect what, no why and how Derivative metrics There is no systematic method Lots of useless metrics We need a systematic method

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Southeast University Dept. of Cs. & Eng. AsiaFI School Basics of FA What is Factor Analysis originated in psychometrics, and is used in behavioral sciences, social sciences, marketing, product management, operations research, and other applied sciences that deal with large quantities of data. a statistical method used to explain variability among observed variables in terms of fewer unobserved variables called factors. The information gained about the interdependencies can be used later to reduce the set of variables in a dataset.

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Southeast University Dept. of Cs. & Eng. AsiaFI School Basics of FA FA Example Spearman a wide variety of mental tests could be explained by a single underlying intelligence factor (a notion now rejected).

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Southeast University Dept. of Cs. & Eng. AsiaFI School Basics of FA Schema for common factor theory

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Southeast University Dept. of Cs. & Eng. AsiaFI School Basics of FA Mathematical model X is a matrix of observable variables F is a m × l matrix of unobservable random variables a ij is factor loading that explain the relationship between the source metrics and the factor metrics

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Southeast University Dept. of Cs. & Eng. AsiaFI School FAME Algorithm Algorithm 1.Select original metrics matrix X ; 2.Get Xs observing experiment data x through measuring process; 3.Test x to determine whether x is fit for factor analysis process. If the answer is yes, then go to the 4th step, else go to the 1st step to reselect metrics; 4.Get factor loading matrix A through factor analysis process; 5.Give each factor semantic meaning through A.

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Southeast University Dept. of Cs. & Eng. AsiaFI School Experiment Experiment Setup Environment Netflow Data aggravated by host Captured at CERNET X Province border Router (Cisco 7609) SPSS 15 Two type of data same time range all-IP traffic data same IP different time traffic data

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Southeast University Dept. of Cs. & Eng. AsiaFI School Experiment Original metrics Metric NamesMeaning ipktsIncoming pkts number opktsOut coming pkts number ioctsIncoming octs ooctsOut coming octs iflowsIncoming flows oflowsOut coming flows iIPsDifferent IP addresses connected to the host oIPsDifferent IP addresses connected by the host iportsDifferent source ports seen in incoming flows oportsDifferent destination ports seen in out coming flows pkts_rThe ratio of ipkts over opkts octs_rThe ratio of iocts over oocts flows_rThe ratio of iflows over oflows IPs_rThe ratio of iIPs over oIPs ports_rThe ratio of iports over oports

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Southeast University Dept. of Cs. & Eng. AsiaFI School Experiment Same time range all-IP traffic data variables Factors 1234 iIPs oIPs iports oports ipkts opkts iocts Oocts Iflows Oflows pks_r octs_r flows_r IPs_r ports_r

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Southeast University Dept. of Cs. & Eng. AsiaFI School Experiment Same time range all-IP traffic data Four factors active factor the level of the user interaction activity with the outside world throughput factor reflects the host throughput from the view of the number of packets, the number of bytes and the number of flows load factor the host tendency of providing or acquiring traffics role factor the host user is client/Server/P2P point

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Southeast University Dept. of Cs. & Eng. AsiaFI School Experiment same IP different time traffic data variable factor 12 ipks opks iocts oocts iflows oflows iIPs oIPs ipors opors rpks rocts rflows rIPs rpors

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Southeast University Dept. of Cs. & Eng. AsiaFI School Experiment same IP different time traffic data two factors active factor the level of the user interaction activity with the outside world role factor the host user is client/Server/P2P point

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Southeast University Dept. of Cs. & Eng. AsiaFI School Conclusion and Future work Conclusion Factor Analysis is a systematic method to exploring derivative metrics Factor metrics can help explain and reduce the source atomic and derivative metrics. Future work how to select source variables for factor analysis how to computer the value of the factor metrics

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Southeast University Dept. of Cs. & Eng. AsiaFI School Reference V. Paxson, G. Almes, J. Mahdavi. Framework for IP Performance Metrics, RFC 2330, May 1998 W. Moore and D. Zuev, Discriminators for use in flow-based classification, Technical report, Intel Research, Cambridge, Mingzhong Zhou, Study of Large-scale Network IP Flows behavior Characteristics and Measurement Algorithms. Phd. Thesis, Southeast University, August 2006.

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Southeast University Dept. of Cs. & Eng. AsiaFI School Questions? Thank You

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