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A Review of Predictive Analytic Applications of Bayesian Network

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@article{IJASEIT1382,
   author = {Mohammad Hafiz Mohd Yusof and Mohd Rosmadi Mokhtar},
   title = {A Review of Predictive Analytic Applications of Bayesian Network},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {6},
   number = {6},
   year = {2016},
   pages = {857--867},
   keywords = {Malware Analysis; Behavioural Analysis; Bayesian Network},
   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 modelling 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.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1382},
   doi = {10.18517/ijaseit.6.6.1382}
}

EndNote

%A Mohd Yusof, Mohammad Hafiz
%A Mokhtar, Mohd Rosmadi
%D 2016
%T A Review of Predictive Analytic Applications of Bayesian Network
%B 2016
%9 Malware Analysis; Behavioural Analysis; Bayesian Network
%! A Review of Predictive Analytic Applications of Bayesian Network
%K Malware Analysis; Behavioural Analysis; Bayesian Network
%X 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 modelling 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.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1382
%R doi:10.18517/ijaseit.6.6.1382
%J International Journal on Advanced Science, Engineering and Information Technology
%V 6
%N 6
%@ 2088-5334

IEEE

Mohammad Hafiz Mohd Yusof and Mohd Rosmadi Mokhtar,"A Review of Predictive Analytic Applications of Bayesian Network," International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 857-867, 2016. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.6.6.1382.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Mohd Yusof, Mohammad Hafiz
AU  - Mokhtar, Mohd Rosmadi
PY  - 2016
TI  - A Review of Predictive Analytic Applications of Bayesian Network
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 6 (2016) No. 6
Y2  - 2016
SP  - 857
EP  - 867
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Malware Analysis; Behavioural Analysis; Bayesian Network
N2  - 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 modelling 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.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1382
DO  - 10.18517/ijaseit.6.6.1382

RefWorks

RT Journal Article
ID 1382
A1 Mohd Yusof, Mohammad Hafiz
A1 Mokhtar, Mohd Rosmadi
T1 A Review of Predictive Analytic Applications of Bayesian Network
JF International Journal on Advanced Science, Engineering and Information Technology
VO 6
IS 6
YR 2016
SP 857
OP 867
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Malware Analysis; Behavioural Analysis; Bayesian Network
AB 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 modelling 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.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=1382
DO  - 10.18517/ijaseit.6.6.1382