文章基本信息

标题：Data Mining and Intrusion Detection Systems
本地全文：下载
作者：Zibusiso Dewa ; Leandros A. Maglaras
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：1
DOI：10.14569/IJACSA.2016.070109
出版社：Science and Information Society (SAI)
摘要：The rapid evolution of technology and the increased connectivity among its components, imposes new cyber-security challenges. To tackle this growing trend in computer attacks and respond threats, industry professionals and academics are joining forces in order to build Intrusion Detection Systems (IDS) that combine high accuracy with low complexity and time efficiency. The present article gives an overview of existing Intrusion Detection Systems (IDS) along with their main principles. Also this article argues whether data mining and its core feature which is knowledge discovery can help in creating Data mining based IDSs that can achieve higher accuracy to novel types of intrusion and demonstrate more robust behaviour compared to traditional IDSs.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; (Intrusion Detection; NSL–KDD; Machine Learning; Datasets; Classifiers; Feature Selection; Waikato Environment for Knowledge Analysis; Anomaly detection; Misuse detection; Data mining)