Research on heteregeneous data for recognizing threat

• Main Authors: Abdullah, Abdul Hanan, Idris, Mohd. Yazid, Stiawan, Deris
• Format: Book Section
• Published: Elsevier 2011
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/29580/; http://www.scopus.com/record/display.url?eid=2-s2.0-80052570325&origin=resultslist&sort=plf-f&src=s&st1=Research+on+heteregeneous+data+for+recognizing+threat&sid=2D01AA8998A37CE27809AE3CCD26EF97.ZmAySxCHIBxxTXbnsoe5w%3a20&sot=b&sdt=b&sl=68&s=TITLE-ABS-KEY
• Institution: Universiti Teknologi Malaysia

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.