Open AccessApplication of High-Dimensional Outlier Mining Based on the Maximum Frequent Pattern Factor in Intrusion Detection
Author(s) -
Limin Shen,
Zhongkui Sun,
Lei Chen,
Jiayin Feng
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/9234084
Subject(s) - intrusion detection system , constant false alarm rate , data mining , computer science , anomaly based intrusion detection system , anomaly detection , local outlier factor , outlier , set (abstract data type) , intrusion , data set , pattern recognition (psychology) , artificial intelligence , geology , geochemistry , programming language
As the Internet applications are growing rapidly, the intrusion detection system is widely used to detect network intrusion effectively. Aiming at the high-dimensional characteristics of data in the intrusion detection system, but the traditional frequent-pattern-based outlier mining algorithm has the problems of difficulty in obtaining complete frequent patterns and high time complexity, the outlier set is further analysed to get the attack pattern of intrusion detection. The NSL-KDD dataset and UNSW-NB15 dataset are used for evaluating the proposed approach by conducting some experiments. The experiment results show that the method has good performance in detection rate, false alarm rate, and recall rate and effectively reduces the time complexity.