Anomaly Extraction in Networks
| Autor: | Ravindra Jagadale, Amit Kanase, Sohan Patil, Gajanan Arsalwad, Naushad Mujawar |
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| Rok vydání: | 2014 |
| Předmět: |
Cloning (programming)
Network packet Computer science business.industry Detector Pattern recognition computer.software_genre Physics::Fluid Dynamics ComputingMethodologies_PATTERNRECOGNITION Flow (mathematics) Histogram Extraction (military) Data mining Artificial intelligence Anomaly (physics) business computer Volume (compression) |
| Zdroj: | International Journal of Computer Trends and Technology. 9:327-330 |
| ISSN: | 2231-2803 2349-0829 |
| DOI: | 10.14445/22312803/ijctt-v9p160 |
| Popis: | The application detects anomaly in network using techniques like histogram, cloning voting, filtering. To extract anomalous flows, one could build a model describing normal flow characteristics and use the model to identify deviating flows. We can compare flows of packets on network with previous flows, like new flows that were not previously observed or flows with significant increase/decrease in their volume. Identify an anomalous flow that combines and consolidates information from multiple histogram-based anomaly detectors (1) (4) (8). Compared to other possible approaches. Build a histogram based detector that (i) applies histogram cloning(1)(4), i.e., maintains multiple randomized histograms to obtain additional views of network traffic(3); and (ii) uses the Kullback-Leibler (KL) distance to detect anomalies. |
| Databáze: | OpenAIRE |
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