Visible to the public Hybrid Approach for Intrusion Detection Using Fuzzy Association Rules

TitleHybrid Approach for Intrusion Detection Using Fuzzy Association Rules
Publication TypeConference Paper
Year of Publication2018
AuthorsDouzi, S., Benchaji, I., ElOuahidi, B.
Conference Name2018 2nd Cyber Security in Networking Conference (CSNet)
Keywordsanomaly detection, anomaly intrusions, Clustering algorithms, computer networks, considerable increase, decision making, decrease resource utilization, Deep Learning, feature extraction, fuzzy association rules, Fuzzy logic, fuzzy logic scheme, fuzzy set theory, Fuzzy sets, hybrid approach, input data space, Internet rapid development, Intrusion detection, intrusion detection system, Metrics, misuse detection, network technologies, pattern clustering, pubcrawl, rapid development, reduced dataset, resilience, Resiliency, security, security of data, Time complexity, Training, weighted fuzzy C-mean clustering algorithm
AbstractRapid development of internet and network technologies has led to considerable increase in number of attacks. Intrusion detection system is one of the important ways to achieve high security in computer networks. However, it have curse of dimensionality which tends to increase time complexity and decrease resource utilization. To improve the ability of detecting anomaly intrusions, a combined algorithm is proposed based on Weighted Fuzzy C-Mean Clustering Algorithm (WFCM) and Fuzzy logic. Decision making is performed in two stages. In the first stage, WFCM algorithm is applied to reduce the input data space. The reduced dataset is then fed to Fuzzy Logic scheme to build the fuzzy sets, membership function and the rules that decide whether an instance represents an anomaly or not.
DOI10.1109/CSNET.2018.8602882
Citation Keydouzi_hybrid_2018