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Anomaly Detection Module for Network Traffic Monitoring in Public Institutions.
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- Additional Information
- Source:
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE
- Publication Information:
Original Publication: Basel, Switzerland : MDPI, c2000-
- Abstract:
It seems to be a truism to say that we should pay more and more attention to network traffic safety. Such a goal may be achieved with many different approaches. In this paper, we put our attention on the increase in network traffic safety based on the continuous monitoring of network traffic statistics and detecting possible anomalies in the network traffic description. The developed solution, called the anomaly detection module, is mostly dedicated to public institutions as the additional component of the network security services. Despite the use of well-known anomaly detection methods, the novelty of the module is based on providing an exhaustive strategy of selecting the best combination of models as well as tuning the models in a much faster offline mode. It is worth emphasizing that combined models were able to achieve 100% balanced accuracy level of specific attack detection.
- References:
Sensors (Basel). 2022 Oct 28;22(21):. (PMID: 36365962)
Foods. 2022 Dec 22;12(1):. (PMID: 36613277)
Comput Commun. 2023 Jan;198:. (PMID: 36741076)
- Contributed Indexing:
Keywords: anomaly detection; cybersecurity; network traffic monitoring
- Publication Date:
Date Created: 20230330 Date Completed: 20230330 Latest Revision: 20230401
- Publication Date:
20231215
- Accession Number:
PMC10059045
- Accession Number:
10.3390/s23062974
- Accession Number:
36991685
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