Image‐based malware classification using VGG19 network and spatial convolutional attention
In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated m...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI
2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/94493/1/AzlanMohdZain2021_ImageBasedMalwareClassification.pdf |
| _version_ | 1796865833507487744 |
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| author | Awan, Mazhar Javed Masood, Osama Ahmed Mohammed, Mazin Abed Yasin, Awais Mohd. Zain, Azlan Damasevicius, Robertas Abdulkareem, Karrar Hameed |
| author_facet | Awan, Mazhar Javed Masood, Osama Ahmed Mohammed, Mazin Abed Yasin, Awais Mohd. Zain, Azlan Damasevicius, Robertas Abdulkareem, Karrar Hameed |
| author_sort | Awan, Mazhar Javed |
| collection | ePrints |
| description | In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state‐of‐the‐art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image‐based classification of 25 well‐known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image‐based malware detection with high performance, despite being simpler as compared to other available solutions. |
| first_indexed | 2024-03-05T21:03:07Z |
| format | Article |
| id | utm.eprints-94493 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T21:03:07Z |
| publishDate | 2021 |
| publisher | MDPI |
| record_format | dspace |
| spelling | utm.eprints-944932022-03-31T15:46:40Z http://eprints.utm.my/94493/ Image‐based malware classification using VGG19 network and spatial convolutional attention Awan, Mazhar Javed Masood, Osama Ahmed Mohammed, Mazin Abed Yasin, Awais Mohd. Zain, Azlan Damasevicius, Robertas Abdulkareem, Karrar Hameed QA75 Electronic computers. Computer science T58.5-58.64 Information technology In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state‐of‐the‐art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image‐based classification of 25 well‐known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image‐based malware detection with high performance, despite being simpler as compared to other available solutions. MDPI 2021-10-01 Article PeerReviewed application/pdf en http://eprints.utm.my/94493/1/AzlanMohdZain2021_ImageBasedMalwareClassification.pdf Awan, Mazhar Javed and Masood, Osama Ahmed and Mohammed, Mazin Abed and Yasin, Awais and Mohd. Zain, Azlan and Damasevicius, Robertas and Abdulkareem, Karrar Hameed (2021) Image‐based malware classification using VGG19 network and spatial convolutional attention. Electronics (Switzerland), 10 (19). pp. 1-19. ISSN 2079-9292 http://dx.doi.org/10.3390/electronics10192444 DOI:10.3390/electronics10192444 |
| spellingShingle | QA75 Electronic computers. Computer science T58.5-58.64 Information technology Awan, Mazhar Javed Masood, Osama Ahmed Mohammed, Mazin Abed Yasin, Awais Mohd. Zain, Azlan Damasevicius, Robertas Abdulkareem, Karrar Hameed Image‐based malware classification using VGG19 network and spatial convolutional attention |
| title | Image‐based malware classification using VGG19 network and spatial convolutional attention |
| title_full | Image‐based malware classification using VGG19 network and spatial convolutional attention |
| title_fullStr | Image‐based malware classification using VGG19 network and spatial convolutional attention |
| title_full_unstemmed | Image‐based malware classification using VGG19 network and spatial convolutional attention |
| title_short | Image‐based malware classification using VGG19 network and spatial convolutional attention |
| title_sort | image based malware classification using vgg19 network and spatial convolutional attention |
| topic | QA75 Electronic computers. Computer science T58.5-58.64 Information technology |
| url | http://eprints.utm.my/94493/1/AzlanMohdZain2021_ImageBasedMalwareClassification.pdf |
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