EFIS: Evolvable-neural-based fuzzy inference system and its application for adaptive network anomaly detection

Muhammad Fermi Pasha, Rahmat Budiarto, Mohammad Syukur, Masashi Yamada

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

16 Citations (Scopus)


This paper presents an application of a new type of fuzzy inference system, denoted as evolvable-neural-based fuzzy inference system (EFIS), for adaptive network anomaly detection in the presence of a concept drift problem. This problem cannot be avoided to happen in every network. It is a problem of modeling the behavior of normal traffic while it keeps changing over time in continuous manner. EFIS can solve the concept drift problem by having dynamic network traffic profile creation and adaptation. The profile is then being further used to detect anomaly. An enhanced evolving clustering method (ECMm), which is employed by EFIS for online network traffic clustering, is also presented. It is demonstrated, through experiments, that EFIS can evolve in a growing network and also successfully detect network traffic anomalies.

Original languageEnglish
Title of host publicationAdvances in Machine Learning and Cybernetics - 4th International Conference, ICMLC 2005, Revised Selected Papers
Number of pages10
Publication statusPublished - 2006
Externally publishedYes
EventInternational Conference on Machine Learning and Cybernetics 2005 - Guangzhou, China
Duration: 18 Aug 200521 Aug 2005
Conference number: 4th
https://link.springer.com/book/10.1007/11739685 (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3930 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Machine Learning and Cybernetics 2005
Abbreviated titleICMLC 2005
Internet address

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