Multi-Fractal Traffic and Anomaly Detection in Computer Communications
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Multi-Fractal Traffic and Anomaly Detection in Computer Communications
Li, Ming
Taylor & Francis Ltd
10/2024
282
Mole
9781032408514
Pré-lançamento - envio 15 a 20 dias após a sua edição
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1. Fractal time series 2. On 1/f noise 3. Power laws of fractal data in cyber-physical networking systems 4. Ergodicity of long-range dependent traffic 5. Predictability of long-range dependent series 6. Long-range dependence and self-similarity of daily traffic with different protocols 7. Stationarity test of traffic 8. Record length requirement of LRD traffic 9. Multi-fractional generalized Cauchy process and its application to traffic 10. Modified multi-fractional Gaussian noise and its application to traffic 11. Traffic simulation 12. Reliably identifying signs of DDOS flood attacks based on traffic pattern recognition 13. Change trend of Hurst parameter of multi-scale traffic under DDOS flood attacks 14. Postscript
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Computer Communications;Network traffic;Time series;Anomaly detection;Traffic modeling and simulation;LRD;Hurst Parameter;Fractional Gaussian Noise;LSS;Weyl Type;Fractal Time Series;LRD Property;Fractal Dimension;Traffic Traces;Vice Versa;Traffic Time Series;Stationarity Test;Autocorrelation Function Estimate;Random Function;Small Time Scales;Sample ACFs;LRD Process;Autocorrelation Function;Riemann Liouville Type;Detrended Fluctuation Analysis;Fractional Order;Multi-fractal Model;Stationary Increment Process;Standard White Noise;Local Irregularity
1. Fractal time series 2. On 1/f noise 3. Power laws of fractal data in cyber-physical networking systems 4. Ergodicity of long-range dependent traffic 5. Predictability of long-range dependent series 6. Long-range dependence and self-similarity of daily traffic with different protocols 7. Stationarity test of traffic 8. Record length requirement of LRD traffic 9. Multi-fractional generalized Cauchy process and its application to traffic 10. Modified multi-fractional Gaussian noise and its application to traffic 11. Traffic simulation 12. Reliably identifying signs of DDOS flood attacks based on traffic pattern recognition 13. Change trend of Hurst parameter of multi-scale traffic under DDOS flood attacks 14. Postscript
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Computer Communications;Network traffic;Time series;Anomaly detection;Traffic modeling and simulation;LRD;Hurst Parameter;Fractional Gaussian Noise;LSS;Weyl Type;Fractal Time Series;LRD Property;Fractal Dimension;Traffic Traces;Vice Versa;Traffic Time Series;Stationarity Test;Autocorrelation Function Estimate;Random Function;Small Time Scales;Sample ACFs;LRD Process;Autocorrelation Function;Riemann Liouville Type;Detrended Fluctuation Analysis;Fractional Order;Multi-fractal Model;Stationary Increment Process;Standard White Noise;Local Irregularity
