Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models
There is a massive growth in malicious software (Malware) development, which causes substantial security threats to individuals and organizations. Cybersecurity researchers makes continuous efforts to defend against these malware risks. This research aims to exploit the significant advantages of Tra...
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doaj-a7026df2e9ea497cb2a7cdf8535f9d032021-07-23T13:29:39ZengMDPI AGApplied Sciences2076-34172021-07-01116446644610.3390/app11146446Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning ModelsWalid El-Shafai0Iman Almomani1Aala AlKhayer2Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi ArabiaSecurity Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi ArabiaSecurity Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh 11586, Saudi ArabiaThere is a massive growth in malicious software (Malware) development, which causes substantial security threats to individuals and organizations. Cybersecurity researchers makes continuous efforts to defend against these malware risks. This research aims to exploit the significant advantages of Transfer Learning (TL) and Fine-Tuning (FT) methods to introduce efficient malware detection in the context of imbalanced families without the need to apply complex features extraction or data augmentation processes. Therefore, this paper proposes a visualized malware multi-classification framework to avoid false positives and imbalanced datasets’ challenges through using the fine-tuned convolutional neural network (CNN)-based TL models. The proposed framework comprises eight different FT CNN models including VGG16, AlexNet, DarkNet-53, DenseNet-201, Inception-V3, Places365-GoogleNet, ResNet-50, and MobileNet-V2. First, the binary files of different malware families were transformed into 2D images and then forwarded to the FT CNN models to detect and classify the malware families. The detection and classification performance was examined on a benchmark Malimg imbalanced dataset using different, comprehensive evaluation metrics. The evaluation results prove the FT CNN models’ significance in detecting malware types with high accuracy that reached 99.97% which also outperforms the performance of related machine learning (ML) and deep learning (DL)-based malware multi-classification approaches tested on the same malware dataset.https://www.mdpi.com/2076-3417/11/14/6446cybersecurity threatsmalware visualizationdetectionclassificationdeep learningmachine learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Walid El-Shafai Iman Almomani Aala AlKhayer |
spellingShingle |
Walid El-Shafai Iman Almomani Aala AlKhayer Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models Applied Sciences cybersecurity threats malware visualization detection classification deep learning machine learning |
author_facet |
Walid El-Shafai Iman Almomani Aala AlKhayer |
author_sort |
Walid El-Shafai |
title |
Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models |
title_short |
Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models |
title_full |
Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models |
title_fullStr |
Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models |
title_full_unstemmed |
Visualized Malware Multi-Classification Framework Using Fine-Tuned CNN-Based Transfer Learning Models |
title_sort |
visualized malware multi-classification framework using fine-tuned cnn-based transfer learning models |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2021-07-01 |
description |
There is a massive growth in malicious software (Malware) development, which causes substantial security threats to individuals and organizations. Cybersecurity researchers makes continuous efforts to defend against these malware risks. This research aims to exploit the significant advantages of Transfer Learning (TL) and Fine-Tuning (FT) methods to introduce efficient malware detection in the context of imbalanced families without the need to apply complex features extraction or data augmentation processes. Therefore, this paper proposes a visualized malware multi-classification framework to avoid false positives and imbalanced datasets’ challenges through using the fine-tuned convolutional neural network (CNN)-based TL models. The proposed framework comprises eight different FT CNN models including VGG16, AlexNet, DarkNet-53, DenseNet-201, Inception-V3, Places365-GoogleNet, ResNet-50, and MobileNet-V2. First, the binary files of different malware families were transformed into 2D images and then forwarded to the FT CNN models to detect and classify the malware families. The detection and classification performance was examined on a benchmark Malimg imbalanced dataset using different, comprehensive evaluation metrics. The evaluation results prove the FT CNN models’ significance in detecting malware types with high accuracy that reached 99.97% which also outperforms the performance of related machine learning (ML) and deep learning (DL)-based malware multi-classification approaches tested on the same malware dataset. |
topic |
cybersecurity threats malware visualization detection classification deep learning machine learning |
url |
https://www.mdpi.com/2076-3417/11/14/6446 |
work_keys_str_mv |
AT walidelshafai visualizedmalwaremulticlassificationframeworkusingfinetunedcnnbasedtransferlearningmodels AT imanalmomani visualizedmalwaremulticlassificationframeworkusingfinetunedcnnbasedtransferlearningmodels AT aalaalkhayer visualizedmalwaremulticlassificationframeworkusingfinetunedcnnbasedtransferlearningmodels |
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