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We propose LD-Sketch, a data structure designed for accurate and scalable traffic anomaly detection using distributed architectures.
The data mining and network management research communities are interested in improving existing score-based network traffic anomaly detection techniques because of ample scopes to increase performance.
The hybrid IDS is obtained by combining packet header anomaly detection (PHAD) and network traffic anomaly detection (NETAD) which are anomaly-based IDSs with the misuse-based IDS Snort which is an open-source project.
In 2011, LastPass publicly disclosed a "traffic anomaly" on their server that they couldn't account for.
Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.
That is why in this paper we introduced a novel network traffic anomaly detection method for a critical railway transportation infrastructure which is utilizing sensor radio network.
Similar(44)
Traffic anomalies arise from network problems, and so detection and diagnosis are useful tools for network managers.
The utilisation of the wavelets permits to easily filter "normal" traffic anomalies such as the periodic trends present in the cellular traffic.
Real-time characterization of network traffic anomalies, such as heavy hitters and heavy changers, is critical for the robustness of operational networks, but its accuracy and scalability are challenged by the ever-increasing volume and diversity of network traffic.
Accurate correlation between radio and traffic anomalies allows enhancing the efficiency of the IDS.
Pan et al. [12] uses a combination of crowd sourcing and social media analysis for identifying traffic anomalies.
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