Authors:
Deris Stiawan
;
Abdul Hanan Abdullah
and
Mohd Yazid Idris
Affiliation:
Universiti Teknologi Malaysia, Malaysia
Keyword(s):
Heterogeneous data, Intrusion prevention and prediction, Data mining.
Related
Ontology
Subjects/Areas/Topics:
Communication and Software Technologies and Architectures
;
Communication Networks and Protocols
;
Computer-Supported Education
;
Distributed and Mobile Software Systems
;
e-Business
;
Enterprise Information Systems
;
Information Technologies Supporting Learning
;
Mobile and Pervasive Computing
;
Security and Privacy
;
Software Engineering
;
Telecommunications
Abstract:
The information increasingly large of volume dataset and multidimensional data has grown rapidly in recent years. Inter-related and update information from security communities or vendor network security has present of content vulnerability and patching bug from new attack (pattern) methods. It given a collection of datasets, we were asked to examine a sample of such data and look for pattern which may exist between certain pattern methods over time. There are several challenges, including handling dynamic data, sparse data, incomplete data, uncertain data, and semistructured/unstructured data. In this paper, we are addressing these challenges and using data mining approach to collecting scattered information in routine update regularly from provider or security community.