A review of predictive analytic applications of Bayesian network

Mohammad Hafiz Mohd Yusof, Mohd Rosmadi Mokhtar

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Malware can be defined as malicious software that infiltrates a network and computer host in a variety of ways, from software flaws to social engineering. Due to the polymorphic and stealth nature of malware attacks, a signature-based analysis that is done statically is no longer sufficient to solve such a problem. Therefore, a behavioral or anomalous analysis will provide a more dynamic approach for the solution. However, recent studies have shown that current behavioral methods at the network-level have several issues such as the inability to predict zero-day attacks, high-level assumptions, non-inferential analysis and performance issues. Other than performance issues, this study has identified common scientific characteristics which are reduced parameter, θ and lack of priori information p(θ) that causes the problems. Previous methods were proposed to address the problem, however, were still unable to resolve the stated scientific hitches. Due to the shortcomings, the Bayesian Network in terms of its probabilistic modeling would be the best method to deal with the stated scientific glitches which also have been proven in the area of Clinical Expert Systems, Artificial Intelligence, and Pattern Recognition. This study will critically review the predictive analytic applications of Bayesian Network model in different research domain such as Clinical Expert Systems, Artificial Intelligence, and Pattern Recognition and discover any potential approach available in the domain of Computer Networks. Based on the review, this paper has identified several Bayesian Network properties which have been used to overcome the abovementioned problems. Those properties will be applied in future studies to model the Behavioral Malware Predictive Analytics.

Original languageEnglish
Pages (from-to)857-867
Number of pages11
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Volume6
Issue number6
DOIs
Publication statusPublished - 2016

Fingerprint

Bayesian networks
Expert Systems
expert systems
artificial intelligence
Artificial Intelligence
Software
Expert systems
couplings
Pattern recognition
Artificial intelligence
engineering
Computer networks
methodology
Research
Defects
Predictive analytics
Malware

Keywords

  • Bayesian Network
  • Behavioural analysis
  • Malware analysis

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Computer Science(all)
  • Engineering(all)

Cite this

A review of predictive analytic applications of Bayesian network. / Yusof, Mohammad Hafiz Mohd; Mokhtar, Mohd Rosmadi.

In: International Journal on Advanced Science, Engineering and Information Technology, Vol. 6, No. 6, 2016, p. 857-867.

Research output: Contribution to journalArticle

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