Preliminary analysis of malware detection in opcode sequences within IoT environment
With the technological development and means of communication, the Internet of Things (IoT) has become an essential role in providing many services in daily life through millions of heterogeneous but interconnected devices and nodes. This development is opening to many security and privacy challenge...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Science Publication
2020
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| Online Access: | http://psasir.upm.edu.my/id/eprint/87259/ http://psasir.upm.edu.my/id/eprint/87259/1/Preliminary%20analysis%20of%20malware%20detection%20in%20opcode.pdf |
| _version_ | 1848860400171352064 |
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| author | Ahmed, Firas Shihab Mustapha, Norwati Mustapha, Aida Kakavand, Mohsen Mohd Foozy, Cik Feresa |
| author_facet | Ahmed, Firas Shihab Mustapha, Norwati Mustapha, Aida Kakavand, Mohsen Mohd Foozy, Cik Feresa |
| author_sort | Ahmed, Firas Shihab |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | With the technological development and means of communication, the Internet of Things (IoT) has become an essential role in providing many services in daily life through millions of heterogeneous but interconnected devices and nodes. This development is opening to many security and privacy challenges that can cause complete network breakdown, bypassed access control or the loss of critical data. This paper attempts to provide a preliminary analysis for malware detection within data generated by IoT-based devices and services in the form of operational codes (Opcode) sequences. Three machine learning algorithms are evaluated and compared for accuracy, precision, recall and F-measure. The results showed that the Random Forest (RF) achieved the best accuracy of 98%, followed by SVM and k-NN, both with 91%. The results are further analyzed based on the Receiver Operating Characteristic (ROC) curve and Precision-Recall curve to further illustrate the difference in performance of all three algorithms when dealing with IoT data. |
| first_indexed | 2025-11-15T12:44:38Z |
| format | Article |
| id | upm-87259 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:44:38Z |
| publishDate | 2020 |
| publisher | Science Publication |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-872592022-01-24T09:24:05Z http://psasir.upm.edu.my/id/eprint/87259/ Preliminary analysis of malware detection in opcode sequences within IoT environment Ahmed, Firas Shihab Mustapha, Norwati Mustapha, Aida Kakavand, Mohsen Mohd Foozy, Cik Feresa With the technological development and means of communication, the Internet of Things (IoT) has become an essential role in providing many services in daily life through millions of heterogeneous but interconnected devices and nodes. This development is opening to many security and privacy challenges that can cause complete network breakdown, bypassed access control or the loss of critical data. This paper attempts to provide a preliminary analysis for malware detection within data generated by IoT-based devices and services in the form of operational codes (Opcode) sequences. Three machine learning algorithms are evaluated and compared for accuracy, precision, recall and F-measure. The results showed that the Random Forest (RF) achieved the best accuracy of 98%, followed by SVM and k-NN, both with 91%. The results are further analyzed based on the Receiver Operating Characteristic (ROC) curve and Precision-Recall curve to further illustrate the difference in performance of all three algorithms when dealing with IoT data. Science Publication 2020-10-05 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/87259/1/Preliminary%20analysis%20of%20malware%20detection%20in%20opcode.pdf Ahmed, Firas Shihab and Mustapha, Norwati and Mustapha, Aida and Kakavand, Mohsen and Mohd Foozy, Cik Feresa (2020) Preliminary analysis of malware detection in opcode sequences within IoT environment. Journal of Computer Science, 16 (9). 1306 - 1318. ISSN 1549-3636; ESSN:1552-6607 https://thescipub.com/abstract/jcssp.2020.1306.1318 10.3844/jcssp.2020.1306.1318 |
| spellingShingle | Ahmed, Firas Shihab Mustapha, Norwati Mustapha, Aida Kakavand, Mohsen Mohd Foozy, Cik Feresa Preliminary analysis of malware detection in opcode sequences within IoT environment |
| title | Preliminary analysis of malware detection in opcode sequences within IoT environment |
| title_full | Preliminary analysis of malware detection in opcode sequences within IoT environment |
| title_fullStr | Preliminary analysis of malware detection in opcode sequences within IoT environment |
| title_full_unstemmed | Preliminary analysis of malware detection in opcode sequences within IoT environment |
| title_short | Preliminary analysis of malware detection in opcode sequences within IoT environment |
| title_sort | preliminary analysis of malware detection in opcode sequences within iot environment |
| url | http://psasir.upm.edu.my/id/eprint/87259/ http://psasir.upm.edu.my/id/eprint/87259/ http://psasir.upm.edu.my/id/eprint/87259/ http://psasir.upm.edu.my/id/eprint/87259/1/Preliminary%20analysis%20of%20malware%20detection%20in%20opcode.pdf |