BibTeX
@article{IJASEIT5045,
author = {Isredza Rahmi A Hamid and Sharmila Subramaniam and Zubaile Abdullah},
title = {Classification of Polymorphic Virus Based on Integrated Features},
journal = {International Journal on Advanced Science, Engineering and Information Technology},
volume = {8},
number = {6},
year = {2018},
pages = {2577--2583},
keywords = {Classification, Polymorphic Virus, Integrated Features.},
abstract = {Standard virus classification relies on the use of virus function, which is a small number of bytes written in assembly language. The addressable problem with current malware intrusion detection and prevention system is having difficulties in detecting unknown and multipath polymorphic computer virus solely based on either static or dynamic features. Thus, this paper presents an effective and efficient polymorphic classification technique based on integrated features. The integrated feature is selected based on Information Gain (IG) rank value between static and dynamic features. Then, all datasets are tested on Naïve Bayes and Random Forest classifiers. We extracted 49 features from 700 polymorphic computer virus samples from Netherland Net Lab and VXHeaven, which includes benign and polymorphic virus function. We spilt the dataset based on 60:40 split ratio sizes for training and testing respectively. Our proposed integrated features manage to achieve 98.9% of accuracy value.},
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=5045},
doi = {10.18517/ijaseit.8.6.5045}
}
EndNote
%A A Hamid, Isredza Rahmi
%A Subramaniam, Sharmila
%A Abdullah, Zubaile
%D 2018
%T Classification of Polymorphic Virus Based on Integrated Features
%B 2018
%9 Classification, Polymorphic Virus, Integrated Features.
%! Classification of Polymorphic Virus Based on Integrated Features
%K Classification, Polymorphic Virus, Integrated Features.
%X Standard virus classification relies on the use of virus function, which is a small number of bytes written in assembly language. The addressable problem with current malware intrusion detection and prevention system is having difficulties in detecting unknown and multipath polymorphic computer virus solely based on either static or dynamic features. Thus, this paper presents an effective and efficient polymorphic classification technique based on integrated features. The integrated feature is selected based on Information Gain (IG) rank value between static and dynamic features. Then, all datasets are tested on Naïve Bayes and Random Forest classifiers. We extracted 49 features from 700 polymorphic computer virus samples from Netherland Net Lab and VXHeaven, which includes benign and polymorphic virus function. We spilt the dataset based on 60:40 split ratio sizes for training and testing respectively. Our proposed integrated features manage to achieve 98.9% of accuracy value.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5045
%R doi:10.18517/ijaseit.8.6.5045
%J International Journal on Advanced Science, Engineering and Information Technology
%V 8
%N 6
%@ 2088-5334
IEEE
Isredza Rahmi A Hamid,Sharmila Subramaniam and Zubaile Abdullah,"Classification of Polymorphic Virus Based on Integrated Features," International Journal on Advanced Science, Engineering and Information Technology, vol. 8, no. 6, pp. 2577-2583, 2018. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.8.6.5045.
RefMan/ProCite (RIS)
TY - JOUR
AU - A Hamid, Isredza Rahmi
AU - Subramaniam, Sharmila
AU - Abdullah, Zubaile
PY - 2018
TI - Classification of Polymorphic Virus Based on Integrated Features
JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 8 (2018) No. 6
Y2 - 2018
SP - 2577
EP - 2583
SN - 2088-5334
PB - INSIGHT - Indonesian Society for Knowledge and Human Development
KW - Classification, Polymorphic Virus, Integrated Features.
N2 - Standard virus classification relies on the use of virus function, which is a small number of bytes written in assembly language. The addressable problem with current malware intrusion detection and prevention system is having difficulties in detecting unknown and multipath polymorphic computer virus solely based on either static or dynamic features. Thus, this paper presents an effective and efficient polymorphic classification technique based on integrated features. The integrated feature is selected based on Information Gain (IG) rank value between static and dynamic features. Then, all datasets are tested on Naïve Bayes and Random Forest classifiers. We extracted 49 features from 700 polymorphic computer virus samples from Netherland Net Lab and VXHeaven, which includes benign and polymorphic virus function. We spilt the dataset based on 60:40 split ratio sizes for training and testing respectively. Our proposed integrated features manage to achieve 98.9% of accuracy value.
UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5045
DO - 10.18517/ijaseit.8.6.5045
RefWorks
RT Journal Article
ID 5045
A1 A Hamid, Isredza Rahmi
A1 Subramaniam, Sharmila
A1 Abdullah, Zubaile
T1 Classification of Polymorphic Virus Based on Integrated Features
JF International Journal on Advanced Science, Engineering and Information Technology
VO 8
IS 6
YR 2018
SP 2577
OP 2583
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Classification, Polymorphic Virus, Integrated Features.
AB Standard virus classification relies on the use of virus function, which is a small number of bytes written in assembly language. The addressable problem with current malware intrusion detection and prevention system is having difficulties in detecting unknown and multipath polymorphic computer virus solely based on either static or dynamic features. Thus, this paper presents an effective and efficient polymorphic classification technique based on integrated features. The integrated feature is selected based on Information Gain (IG) rank value between static and dynamic features. Then, all datasets are tested on Naïve Bayes and Random Forest classifiers. We extracted 49 features from 700 polymorphic computer virus samples from Netherland Net Lab and VXHeaven, which includes benign and polymorphic virus function. We spilt the dataset based on 60:40 split ratio sizes for training and testing respectively. Our proposed integrated features manage to achieve 98.9% of accuracy value.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=5045
DO - 10.18517/ijaseit.8.6.5045
