Malware detection system using cloud sandbox, machine learning
Today's internet continues to move forward, and with it comes the development of many applications. Therefore, these applications are also directly accessible via the Internet, which makes it one of the important things these days. In addition to this, these applications are sometimes developed...
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/35168/1/Malware%20detection%20system%20using%20cloud%20sandbox.pdf |
| _version_ | 1796995286281748480 |
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| author | Mail, Mohd Azuwan Efendy Ab Razak, Mohd Faizal Ab Rahman, Munirah |
| author_facet | Mail, Mohd Azuwan Efendy Ab Razak, Mohd Faizal Ab Rahman, Munirah |
| author_sort | Mail, Mohd Azuwan Efendy |
| collection | UMP |
| description | Today's internet continues to move forward, and with it comes the development of many applications. Therefore, these applications are also directly accessible via the Internet, which makes it one of the important things these days. In addition to this, these applications are sometimes developed as software that can be installed on users computers, laptops and even smartphones, which often attracts many attackers to compromise their computers with malware that is unintentionally installed in the computer. Gadgets and even computer systems. computer background. Many solutions have been employed to detect if these malware are installed. This paper aims to evaluate and study the effectiveness of machine learning methods in detecting and classifying malware being installed. This paper employs heuristics and machine learning classifiers to identify malware attacks detected in each website or software application. The study compares 3 classifiers to find the best machine learning classifier for detecting malware attacks. Prove that the cloud sandbox can achieve a high detection accuracy of 99.8% true positive rate value when identifying malware attacks? Use website features. Results show that Cloud Sandbox is an effective classifier for detecting malware attacks. |
| first_indexed | 2024-03-06T13:00:05Z |
| format | Article |
| id | UMPir35168 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:00:05Z |
| publishDate | 2022 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir351682022-09-12T01:27:58Z http://umpir.ump.edu.my/id/eprint/35168/ Malware detection system using cloud sandbox, machine learning Mail, Mohd Azuwan Efendy Ab Razak, Mohd Faizal Ab Rahman, Munirah QA76 Computer software Today's internet continues to move forward, and with it comes the development of many applications. Therefore, these applications are also directly accessible via the Internet, which makes it one of the important things these days. In addition to this, these applications are sometimes developed as software that can be installed on users computers, laptops and even smartphones, which often attracts many attackers to compromise their computers with malware that is unintentionally installed in the computer. Gadgets and even computer systems. computer background. Many solutions have been employed to detect if these malware are installed. This paper aims to evaluate and study the effectiveness of machine learning methods in detecting and classifying malware being installed. This paper employs heuristics and machine learning classifiers to identify malware attacks detected in each website or software application. The study compares 3 classifiers to find the best machine learning classifier for detecting malware attacks. Prove that the cloud sandbox can achieve a high detection accuracy of 99.8% true positive rate value when identifying malware attacks? Use website features. Results show that Cloud Sandbox is an effective classifier for detecting malware attacks. Penerbit UMP 2022-07 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/35168/1/Malware%20detection%20system%20using%20cloud%20sandbox.pdf Mail, Mohd Azuwan Efendy and Ab Razak, Mohd Faizal and Ab Rahman, Munirah (2022) Malware detection system using cloud sandbox, machine learning. International Journal of Software Engineering & Computer Sciences (IJSECS), 8 (2). pp. 25-32. ISSN 2289-8522. (Published) https://doi.org/10.15282/ijsecs.8.2.2022.3.0100 https://doi.org/10.15282/ijsecs.8.2.2022.3.0100 |
| spellingShingle | QA76 Computer software Mail, Mohd Azuwan Efendy Ab Razak, Mohd Faizal Ab Rahman, Munirah Malware detection system using cloud sandbox, machine learning |
| title | Malware detection system using cloud sandbox, machine learning |
| title_full | Malware detection system using cloud sandbox, machine learning |
| title_fullStr | Malware detection system using cloud sandbox, machine learning |
| title_full_unstemmed | Malware detection system using cloud sandbox, machine learning |
| title_short | Malware detection system using cloud sandbox, machine learning |
| title_sort | malware detection system using cloud sandbox machine learning |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/35168/1/Malware%20detection%20system%20using%20cloud%20sandbox.pdf |
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