Visible to the public Fuzzy Logic based Network Intrusion Detection Systems

TitleFuzzy Logic based Network Intrusion Detection Systems
Publication TypeConference Paper
Year of Publication2020
AuthorsJohanyák, Z. C.
Conference Name2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI)
Date PublishedJan. 2020
ISBN Number978-1-7281-3149-8
Keywordsabnormal network traffic, computer network, computer network security, computer networks, Cyber physical system, electronic communication, false positive classification, feature extraction, Fuzzy logic, fuzzy logic based solutions, fuzzy rule interpolation, interpolation, intrusion detection system, malicious activities, Metrics, network connectivity, network intrusion detection systems, NIDSs, noisy data, normal network traffic, pubcrawl, resilience, Resiliency, rule base generation steps, security

Plenary Talk Our everyday life is more and more dependent on electronic communication and network connectivity. However, the threats of attacks and different types of misuse increase exponentially with the expansion of computer networks. In order to alleviate the problem and to identify malicious activities as early as possible Network Intrusion Detection Systems (NIDSs) have been developed and intensively investigated. Several approaches have been proposed and applied so far for these systems. It is a common challenge in this field that often there are no crisp boundaries between normal and abnormal network traffic, there are noisy or inaccurate data and therefore the investigated traffic could represent both attack and normal communication. Fuzzy logic based solutions could be advantageous owing to their capability to define membership levels in different classes and to do different operations with results ensuring reduced false positive and false negative classification compared to other approaches. In this presentation, after a short introduction of NIDSs a survey will be done on typical fuzzy logic based solutions followed by a detailed description of a fuzzy rule interpolation based IDS. The whole development process, i.e. data preprocessing, feature extraction, rule base generation steps are covered as well.

Citation Keyjohanyak_fuzzy_2020