MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network
Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increase...
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2022-06-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
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| Online Access: | https://www.ijimai.org/journal/bibcite/reference/3021 |
| _version_ | 1828554600129495040 |
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| author | Mahendra Deore Uday Kulkarni |
| author_facet | Mahendra Deore Uday Kulkarni |
| author_sort | Mahendra Deore |
| collection | DOAJ |
| description | Technological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased ‘Threat’ of an ‘Attack’. Attackers are targeting these devices, as it may provide an easier ‘backdoor entry to the users’ network’.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method. |
| first_indexed | 2024-12-12T05:38:40Z |
| format | Article |
| id | doaj.art-fea80153f3da4dd984c2901904f5341a |
| institution | Directory Open Access Journal |
| issn | 1989-1660 |
| language | English |
| last_indexed | 2024-12-12T05:38:40Z |
| publishDate | 2022-06-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-fea80153f3da4dd984c2901904f5341a2022-12-22T00:36:01ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602022-06-017414616210.9781/ijimai.2021.09.005ijimai.2021.09.005MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural NetworkMahendra DeoreUday KulkarniTechnological advancement of smart devices has opened up a new trend: Internet of Everything (IoE), where all devices are connected to the web. Large scale networking benefits the community by increasing connectivity and giving control of physical devices. On the other hand, there exists an increased ‘Threat’ of an ‘Attack’. Attackers are targeting these devices, as it may provide an easier ‘backdoor entry to the users’ network’.MALicious softWARE (MalWare) is a major threat to user security. Fast and accurate detection of malware attacks are the sine qua non of IoE, where large scale networking is involved. The paper proposes use of a visualization technique where the disassembled malware code is converted into gray images, as well as use of Image Similarity based Statistical Parameters (ISSP) such as Normalized Cross correlation (NCC), Average difference (AD), Maximum difference (MaxD), Singular Structural Similarity Index Module (SSIM), Laplacian Mean Square Error (LMSE), MSE and PSNR. A vector consisting of gray image with statistical parameters is trained using a Faster Region proposals Convolution Neural Network (F-RCNN) classifier. The experiment results are promising as the proposed method includes ISSP with F-RCNN training. Overall training time of learning the semantics of higher-level malicious behaviors is less. Identification of malware (testing phase) is also performed in less time. The fusion of image and statistical parameter enhances system performance with greater accuracy. The benchmark database from Microsoft Malware Classification challenge has been used to analyze system performance, which is available on the Kaggle website. An overall average classification accuracy of 98.12% is achieved by the proposed method.https://www.ijimai.org/journal/bibcite/reference/3021malwareconvolutional neural network (cnn)faster rcnn (f-rcnn)classificationmalware staticdynamic analysis |
| spellingShingle | Mahendra Deore Uday Kulkarni MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network International Journal of Interactive Multimedia and Artificial Intelligence malware convolutional neural network (cnn) faster rcnn (f-rcnn) classification malware static dynamic analysis |
| title | MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network |
| title_full | MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network |
| title_fullStr | MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network |
| title_full_unstemmed | MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network |
| title_short | MDFRCNN: Malware Detection using Faster Region Proposals Convolution Neural Network |
| title_sort | mdfrcnn malware detection using faster region proposals convolution neural network |
| topic | malware convolutional neural network (cnn) faster rcnn (f-rcnn) classification malware static dynamic analysis |
| url | https://www.ijimai.org/journal/bibcite/reference/3021 |
| work_keys_str_mv | AT mahendradeore mdfrcnnmalwaredetectionusingfasterregionproposalsconvolutionneuralnetwork AT udaykulkarni mdfrcnnmalwaredetectionusingfasterregionproposalsconvolutionneuralnetwork |