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 |
| Summary: | 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%. |
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