Malware detection using n-gram with TF-IDF weighting
In this era of technology, computers and networks are exposed to malwares. Malwares are also known as malicious software. Malwares are created to disrupt, destroy or to gain authorization in access in a computer system. There are different types of software and methods that have been implemented tha...
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| Format: | Undergraduates Project Papers |
| Language: | English |
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2018
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| Online Access: | http://umpir.ump.edu.my/id/eprint/26839/ http://umpir.ump.edu.my/id/eprint/26839/ http://umpir.ump.edu.my/id/eprint/26839/1/Malware%20detection%20using%20n-gram%20with%20TF-IDF.pdf |
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ump-26839 |
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eprints |
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ump-268392019-12-12T09:02:53Z http://umpir.ump.edu.my/id/eprint/26839/ Malware detection using n-gram with TF-IDF weighting Natasha, Zainal QA76 Computer software In this era of technology, computers and networks are exposed to malwares. Malwares are also known as malicious software. Malwares are created to disrupt, destroy or to gain authorization in access in a computer system. There are different types of software and methods that have been implemented that are used to detect different types of malware. Powerful malware that was implemented may not get easily detected. Different kinds of anti-virus and methods were used, nevertheless the problem is that this may not fully detect the malware as malwares now a days are hard to detect. The objectives of this research is to identify the attributes of malware, to develop a conceptual model of malware detection using n-gram and TF-IDF and to evaluate the model of malware detection. The scope for this research are dataset, method and evaluation testing and measurements. The methodology are literature review based on previous research, identifying the attributes of malware, developing the conceptual model and lastly, evaluating the conceptual model. The model is implemented by using Python programming language. By using this method, the expected result of this system is based on the n-gram and TF-IDF, thus malware could be detected. 2018-12 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26839/1/Malware%20detection%20using%20n-gram%20with%20TF-IDF.pdf Natasha, Zainal (2018) Malware detection using n-gram with TF-IDF weighting. Faculty of Computer System & Software Engineering, Universiti Malaysia Pahang. http://fypro.ump.edu.my/ethesis/index.php |
| repository_type |
Digital Repository |
| institution_category |
Local University |
| institution |
Universiti Malaysia Pahang |
| building |
UMP Institutional Repository |
| collection |
Online Access |
| language |
English |
| topic |
QA76 Computer software |
| spellingShingle |
QA76 Computer software Natasha, Zainal Malware detection using n-gram with TF-IDF weighting |
| description |
In this era of technology, computers and networks are exposed to malwares. Malwares are also known as malicious software. Malwares are created to disrupt, destroy or to gain authorization in access in a computer system. There are different types of software and methods that have been implemented that are used to detect different types of malware. Powerful malware that was implemented may not get easily detected. Different kinds of anti-virus and methods were used, nevertheless the problem is that this may not fully detect the malware as malwares now a days are hard to detect. The objectives of this research is to identify the attributes of malware, to develop a conceptual model of malware detection using n-gram and TF-IDF and to evaluate the model of malware detection. The scope for this research are dataset, method and evaluation testing and measurements. The methodology are literature review based on previous research, identifying the attributes of malware, developing the conceptual model and lastly, evaluating the conceptual model. The model is implemented by using Python programming language. By using this method, the expected result of this system is based on the n-gram and TF-IDF, thus malware could be detected. |
| format |
Undergraduates Project Papers |
| author |
Natasha, Zainal |
| author_facet |
Natasha, Zainal |
| author_sort |
Natasha, Zainal |
| title |
Malware detection using n-gram with TF-IDF weighting |
| title_short |
Malware detection using n-gram with TF-IDF weighting |
| title_full |
Malware detection using n-gram with TF-IDF weighting |
| title_fullStr |
Malware detection using n-gram with TF-IDF weighting |
| title_full_unstemmed |
Malware detection using n-gram with TF-IDF weighting |
| title_sort |
malware detection using n-gram with tf-idf weighting |
| publishDate |
2018 |
| url |
http://umpir.ump.edu.my/id/eprint/26839/ http://umpir.ump.edu.my/id/eprint/26839/ http://umpir.ump.edu.my/id/eprint/26839/1/Malware%20detection%20using%20n-gram%20with%20TF-IDF.pdf |
| first_indexed |
2023-09-18T22:42:02Z |
| last_indexed |
2023-09-18T22:42:02Z |
| _version_ |
1777416979156566016 |