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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/3476/1/KP%202020%20%2874%29.pdf |
| _version_ | 1825637082960232448 |
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| 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 |
| collection | UTHM |
| 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%. |
| first_indexed | 2024-03-05T21:45:54Z |
| format | Conference or Workshop Item |
| id | uthm.eprints-3476 |
| institution | Universiti Tun Hussein Onn Malaysia |
| language | English |
| last_indexed | 2024-03-05T21:45:54Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | 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 |
| 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 |
| title | 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_short | Classification of metamorphic virus using n-grams signatures |
| title_sort | classification of metamorphic virus using n grams signatures |
| topic | QA75-76.95 Calculating machines |
| url | http://eprints.uthm.edu.my/3476/1/KP%202020%20%2874%29.pdf |
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