We present 'D-CAD,' a novel divergence-measure based classification method for anomaly detection in network traffic. The D-CAD method identifies anomalies ...
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A Divergence-measure Based Classification Method for Detecting Anomalies in Network Traffic. Kiran S. Balagani Vir V. Phoha Gopi K. Kuchimanchi.
In the paper the binary classification-based approach to network traffic safety monitoring is presented.
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Aug 15, 2024 · The Jensen-Shannon divergence is used for detecting deviations between previously established and current distributions of network traffic. We ...
Aug 9, 2023 · Network anomaly detection can be divided into four categories: statistics-based, time series-based, sketch-based, and machine learning-based ...
We develop a behavior-based anomaly detection method that detects network anomalies by comparing the current network traffic against a baseline distribution.
May 5, 2024 · We propose a Kullback-Leibler Divergence (KLD) filter to extract anomalies within data series generated by a broad class of proximity sensors, ...
This paper presents review on all the work done related to Network Traffic Management since 1993 to 2013 in various fields like artificial intelligence, neural ...
Anomaly-Based Detection - an overview | ScienceDirect Topics
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Anomaly-based detection is defined as a technique that compares ongoing patterns or behaviors with expected ones, identifying any deviations as anomalies, ...
Oct 22, 2024 · We compare three divergence measures (Hellinger Distance, Chi-square and Power divergence) to analyze their detection accuracy. The performance ...
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