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An Entropy-Based Network Anomaly Detection Method

Systems' Department, Military Communication Institute, ul. Warszawska 22a, 05-130 Zegrze, Poland
Department of Applied Computer Science, AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Krakow, Poland
Author to whom correspondence should be addressed.
Academic Editors: James Park and Wanlei Zhou
Entropy 2015, 17(4), 2367-2408;
Received: 28 February 2015 / Revised: 15 April 2015 / Accepted: 16 April 2015 / Published: 20 April 2015
Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i) preparation of a concept of original entropy-based network anomaly detection method, (ii) implementation of the method, (iii) preparation of original dataset, (iv) evaluation of the method. View Full-Text
Keywords: anomaly detection; entropy; malware detection anomaly detection; entropy; malware detection
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MDPI and ACS Style

Bereziński, P.; Jasiul, B.; Szpyrka, M. An Entropy-Based Network Anomaly Detection Method. Entropy 2015, 17, 2367-2408.

AMA Style

Bereziński P, Jasiul B, Szpyrka M. An Entropy-Based Network Anomaly Detection Method. Entropy. 2015; 17(4):2367-2408.

Chicago/Turabian Style

Bereziński, Przemysław, Bartosz Jasiul, and Marcin Szpyrka. 2015. "An Entropy-Based Network Anomaly Detection Method" Entropy 17, no. 4: 2367-2408.

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