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Please use this identifier to cite or link to this item: http://dspace.vgtu.lt/handle/1/3913

Title: Application of Histogram-Based Outlier Scores to Detect Computer Network Anomalies
Authors: Paulauskas, Nerijus
Baškys, Algirdas
Keywords: anomaly detection
intrusion detection
network security
histogram-based outlier score (HBOS)
Issue Date: 2019
Publisher: MDPI
Citation: Paulauskas, N.; Baskys, A. Application of Histogram-Based Outlier Scores to Detect Computer Network Anomalies. Electronics 2019, 8, 1251.
Series/Report no.: 8;11
Abstract: Misuse activity in computer networks constantly creates new challenges and difficulties to ensure data confidentiality, integrity, and availability. The capability to identify and quickly stop the attacks is essential, as the undetected and successful attack may cause losses of critical resources. The anomaly-based intrusion detection system (IDS) is a valuable security tool that is capable of detecting new, previously unseen attacks. Anomaly-based IDS sends an alarm when it detects an event that deviates from the behavior characterized as normal. This paper analyses the use of the histogram-based outlier score (HBOS) to detect anomalies in the computer network. Experimental results of different histogram creation methods and the influence of the number of bins on the performance of anomaly detection are presented. Experiments were conducted using an NSL-KDD dataset.
Description: This article belongs to the Special Issue State-of-the-Art of Cyber Security
URI: http://dspace.vgtu.lt/handle/1/3913
ISSN: 2079-9292
Appears in Collections:Moksliniai straipsniai / Research articles

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