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Classification of metamorphic virus using n-grams signatures

Metamorphic virus has a capability to change, translate, and rewrite its own code once infected the system to bypass detection. The computer system then can be seriously damage by this undetected metamorphic virus. Due to this, it is very vital to design a metamorphic virus classification model...

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Main Authors: A Hamid, Isredza Rahmi, Md Sani, Nur Sakinah, Abdullah, Zubaile, Mohd Foozy, Cik Feresa, Kipli, Kuryati
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
QA75-76.95 Calculating machines
Online Access:http://eprints.uthm.edu.my/3476/1/KP%202020%20%2874%29.pdf
http://eprints.uthm.edu.my/3476/
http://dx.doi.org/10.1007/978-3-030-36056-6_14
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spelling my.uthm.eprints.34762021-11-02T03:26:38Z http://eprints.uthm.edu.my/3476/ Classification of metamorphic virus using n-grams signatures A Hamid, Isredza Rahmi Md Sani, Nur Sakinah Abdullah, Zubaile Mohd Foozy, Cik Feresa Kipli, Kuryati QA75-76.95 Calculating machines Metamorphic virus has a capability to change, translate, and rewrite its own code once infected the system to bypass detection. The computer system then can be seriously damage by this undetected metamorphic virus. Due to this, it is very vital to design a metamorphic virus classification model that can detect this virus. This paper focused on detection of metamorphic virus using Term Frequency Inverse Document Frequency (TF-IDF) technique. This research was conducted using Second Generation virus dataset. The first step is the classification model to cluster the metamorphic virus using TF-IDF technique. Then, the virus cluster is evaluated using Naïve Bayes algorithm in terms of accuracy using performance metric. The types of virus classes and features are extracted from bi-gram assembly language. The result shows that the proposed model was able to classify metamorphic virus using TF-IDF with optimal number of virus class with average accuracy of 94.2%. 2020 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/3476/1/KP%202020%20%2874%29.pdf A Hamid, Isredza Rahmi and Md Sani, Nur Sakinah and Abdullah, Zubaile and Mohd Foozy, Cik Feresa and Kipli, Kuryati (2020) Classification of metamorphic virus using n-grams signatures. In: Fourth International Conference on Soft Computing and Data Mining (SCDM 2020), January 22–⁠23, 2020, Melaka, Malaysia. http://dx.doi.org/10.1007/978-3-030-36056-6_14
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
topic QA75-76.95 Calculating machines
spellingShingle QA75-76.95 Calculating machines
A Hamid, Isredza Rahmi
Md Sani, Nur Sakinah
Abdullah, Zubaile
Mohd Foozy, Cik Feresa
Kipli, Kuryati
Classification of metamorphic virus using n-grams signatures
description Metamorphic virus has a capability to change, translate, and rewrite its own code once infected the system to bypass detection. The computer system then can be seriously damage by this undetected metamorphic virus. Due to this, it is very vital to design a metamorphic virus classification model that can detect this virus. This paper focused on detection of metamorphic virus using Term Frequency Inverse Document Frequency (TF-IDF) technique. This research was conducted using Second Generation virus dataset. The first step is the classification model to cluster the metamorphic virus using TF-IDF technique. Then, the virus cluster is evaluated using Naïve Bayes algorithm in terms of accuracy using performance metric. The types of virus classes and features are extracted from bi-gram assembly language. The result shows that the proposed model was able to classify metamorphic virus using TF-IDF with optimal number of virus class with average accuracy of 94.2%.
format Conference or Workshop Item
author A Hamid, Isredza Rahmi
Md Sani, Nur Sakinah
Abdullah, Zubaile
Mohd Foozy, Cik Feresa
Kipli, Kuryati
author_facet A Hamid, Isredza Rahmi
Md Sani, Nur Sakinah
Abdullah, Zubaile
Mohd Foozy, Cik Feresa
Kipli, Kuryati
author_sort A Hamid, Isredza Rahmi
title Classification of metamorphic virus using n-grams signatures
title_short Classification of metamorphic virus using n-grams signatures
title_full Classification of metamorphic virus using n-grams signatures
title_fullStr Classification of metamorphic virus using n-grams signatures
title_full_unstemmed Classification of metamorphic virus using n-grams signatures
title_sort classification of metamorphic virus using n-grams signatures
publishDate 2020
url http://eprints.uthm.edu.my/3476/1/KP%202020%20%2874%29.pdf
http://eprints.uthm.edu.my/3476/
http://dx.doi.org/10.1007/978-3-030-36056-6_14
_version_ 1738581128035434496
score 13.211869

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