Ten lectures on wavelets
Self-similarity in World Wide Web traffic: evidence and possible causes
IEEE/ACM Transactions on Networking (TON)
Mining in a data-flow environment: experience in network intrusion detection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A non-instrusive, wavelet-based approach to detecting network performance problems
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Flash crowds and denial of service attacks: characterization and implications for CDNs and web sites
Proceedings of the 11th international conference on World Wide Web
A signal analysis of network traffic anomalies
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Efficient Time Series Matching by Wavelets
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
A wavelet based multiresolution algorithm for rotation invariant feature extraction
Pattern Recognition Letters
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
Network intrusion detection using wavelet analysis
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Wavelet analysis of long-range-dependent traffic
IEEE Transactions on Information Theory
Local anomaly detection for mobile network monitoring
Information Sciences: an International Journal
Supervised classification of share price trends
Information Sciences: an International Journal
Improved anomaly detection using block-matching denoising
Computer Communications
Application traffic classification at the early stage by characterizing application rounds
Information Sciences: an International Journal
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The present work integrates the multiscale transform provided by the wavelets and singular value decomposition (SVD) for the detection of anomaly in self-similar network data. The algorithm proposed in this paper uses the properties of singular value decomposition (SVD) of a matrix whose elements are local energies of wavelet coefficients at different scales. Unlike existing techniques, our method determines both the presence (i.e., the time intervals in which anomaly occurs) and the nature of anomaly (i.e., anomaly of bursty type, long or short duration, etc.) in network data. It uses the diagonal, left and right singular matrices obtained in SVD to determine the number of scales of self-similarity, location and scales of anomaly in data, respectively. Our simulation work on different data sets demonstrates that the method performs better than the existing anomaly detection methods proposed for self-similar data.