Next Article in Journal
A Vehicle Target Recognition Algorithm for Wide-Angle SAR Based on Joint Feature Set Matching
Previous Article in Journal
Melody Extraction and Encoding Method for Generating Healthcare Music Automatically
Previous Article in Special Issue
The Application of a New Secure Software Development Life Cycle (S-SDLC) with Agile Methodologies
Open AccessArticle

Application of Histogram-Based Outlier Scores to Detect Computer Network Anomalies

1
Department of Computer Science and Communications Technologies, Vilnius Gediminas Technical University, Naugarduko st. 41, LT-03227 Vilnius, Lithuania
2
Center for Physical Sciences and Technology, Sauletekio al. 3, LT-10257 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(11), 1251; https://doi.org/10.3390/electronics8111251
Received: 20 September 2019 / Revised: 24 October 2019 / Accepted: 30 October 2019 / Published: 1 November 2019
(This article belongs to the Special Issue State-of-the-Art of Cyber Security)
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. View Full-Text
Keywords: anomaly detection; intrusion detection; network security; histogram-based outlier score (HBOS) anomaly detection; intrusion detection; network security; histogram-based outlier score (HBOS)
Show Figures

Figure 1

MDPI and ACS Style

Paulauskas, N.; Baskys, A. Application of Histogram-Based Outlier Scores to Detect Computer Network Anomalies. Electronics 2019, 8, 1251.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop