Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features
Abstract
:1. Introduction
- The conversion of malware binaries into the grayscale image.
- Data augmentation performed on malware images to overcome the data imbalance within the malware datasets for robust feature extraction to enhance the classifier performance.
- An optimized multimodal feature representation to combine the segmentation-based fractal texture analysis (SFTA) features [15] and deep convolutional neural network (DCNN) features into a single feature vector to obtain a robust malware classification model.
2. Related Work
3. Methodology
3.1. Outline of Methodology
3.2. Visualization of Binary to a Grayscale Image
3.3. Image Augmentation
Algorithm 1. Malware Image Augmentation |
Input: Malware images , image flipping (Vertical and Horizontal) and rotation where is the rotation angle. Output: Rotation of malware image and flipped malware image . 1: for each Image do 2: for to 3: Angle of rotation to 4: end for 5: for 6: Flipping 7: end for 8: end for 9: return and |
3.4. Feature Extraction
3.4.1. Texture Feature (SFTA)
Algorithm 2. SFTA Feature Extraction Algorithm |
Require: Grayscale image and two thresholds and . Ensure: SFTA feature vector . 1: MultiLevelOtsus 2: 3: 4: 5: for do 6: Two Thresholds 7: Find Borders 8: Box Counting 9: MeanGrayLevel 10: Pixel Count 11: 12: end for 13: return |
3.4.2. Deep Convolution Neural Network (DCNN)
3.4.3. DCNN Feature Extraction and Fusion
3.5. Feature Selection
4. Results and Analysis
4.1. Dataset
4.2. Settings
4.3. Results
4.3.1. Experiment 1: With Imbalanced Data and SFTA Features without Feature Fusion and Feature Optimization
4.3.2. Experiment 2: With Balanced (Augmented) Data and SFTA Features
4.3.3. Experiment 3: With Imbalanced Data and Using Featured Obtained from a Pre-Trained Network Model
4.3.4. Experiment 4: With Balanced Data and Fusion of SFTA and Pre-Trained Network Features
4.4. Statistical Analysis
4.5. Comparison of the Results of the Proposed Technique with Other Existing Methods
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Case Study | Year | Data Analysis | Dataset | Classification Approach | Accuracy |
---|---|---|---|---|---|
Malicious Code Localization [41] | 2018 | Static | Malware Dataset Benign Dataset Wild Dataset | Graph Kernels Support Vector Machine (SVM) | 94% |
Malware Variants Classified Behaviors [42] | 2018 | Dynamic | 1220 malware samples | SVM, Decision Tree, Naïve Bayes | 88.3% |
Detection of Android Native Code Malware Variants [43] | 2018 | Static | Benign Dataset Malware Dataset | SVM | 93.22% |
Detecting and Classifying Android Malware [44] | 2017 | Static | Benign Dataset Malware Dataset | SVM | 90% |
Android Malware Detection System Based on Machine Learning [45] | 2018 | Hybrid | Benign Dataset Malware Dataset | SVM | 95.2% |
Malicious Code Variants Based on Deep Learning [12] | 2018 | Hybrid | Malware Dataset | SVM | 94.5% |
Image-Based malware classification [33] | 2020 | Static | Malware Dataset | Ensemble of convolutional neural networks | 99.5% |
Operations | Flipping | Scaling | Rotation |
---|---|---|---|
Matrix transform |
Classifier | Recall (%) | Accuracy (%) | AUC (%) | ER (%) |
---|---|---|---|---|
Cubic Support Vector Machine (SVM) | 94.0 | 92.1 | 90.0 | 7.9 |
Decision Tree | 36.0 | 72.4 | 81.0 | 27.6 |
Weighted k-Nearest Neighbor (KNN) | 86.0 | 87.2 | 75.0 | 12.8 |
Fine Gaussian | 49.0 | 81.4 | 93.1 | 18.6 |
Fine KNN | 95.0 | 93.7 | 92.0 | 6.3 |
Boosted Decision Tree | 85.9 | 85.2 | 92.0 | 14.5 |
Adaboost | 91.0 | 90.2 | 89.0 | 9.8 |
Linear Discriminate | 76.0 | 74.4 | 78.0 | 25.6 |
Quadratic SVM | 96.0 | 95.2 | 90.0 | 4.8 |
Classifier | Recall (%) | Accuracy (%) | AUC (%) | ER (%) |
---|---|---|---|---|
Gaussian SVM | 98.0 | 98.5 | 100.0 | 1.5 |
Decision Tree | 72.0 | 76.8 | 99.0 | 23.2 |
Weighted KNN | 95.0 | 96.3 | 99.0 | 3.7 |
Fine Gaussian | 94.0 | 95.7 | 100.0 | 4.3 |
Boosted Decision Tree | 98.0 | 98.4 | 100.0 | 1.6 |
Adaboost | 96.0 | 95.2 | 99.0 | 4.8 |
Linear Discriminate | 97.6 | 99.3 | 60.0 | 0.7 |
Cubic SVM | 98.0 | 98.2 | 100.0 | 1.8 |
Classifier | Recall (%) | Accuracy (%) | AUC (%) | ER (%) |
---|---|---|---|---|
Linear SVM | 96.0 | 97.5 | 85.0 | 2.5 |
Fine Decision Tree | 96.0 | 97.4 | 85.0 | 2.6 |
Weighted KNN | 66.0 | 44.6 | 60.0 | 55.4 |
Fine KNN | 67.0 | 54.8 | 63.0 | 45.2 |
Cosine KNN | 97.0 | 98.7 | 88.0 | 1.3 |
Boosted Decision Tree | 78.0 | 68.9 | 72.0 | 31.1 |
Quadratic SVM | 96.0 | 97.5 | 85.0 | 2.5 |
Linear Discriminate | 96.0 | 98.1 | 88.0 | 1.9 |
Cosine Decision Tree | 72.0 | 58.6 | 63.0 | 41.8 |
Medium Decision Tree | 69.0 | 51.4 | 60.0 | 48.6 |
Cubic SVM | 97.0 | 98.3 | 88.0 | 1.7 |
Classifier | Recall (%) | Accuracy (%) | AUC (%) | ER (%) |
---|---|---|---|---|
Linear SVM | 98.0 | 98.5 | 100.0 | 1.5 |
Fine Decision Tree | 77.0 | 82.8 | 97.0 | 17.2 |
Weighted KNN | 95.0 | 96.3 | 99.0 | 4.3 |
Medium KNN | 93.0 | 94.7 | 99.0 | 6.7 |
Fine KNN | 97.0 | 97.7 | 98.0 | 3.7 |
Cosine KNN | 96.0 | 96.9 | 99 | 4.9 |
Boosted Decision Tree | 97.0 | 72.8 | 99.0 | 27.2 |
Quadratic SVM | 95.0 | 99.1 | 100.0 | 1.0 |
Linear Discriminate | 97.0 | 97.6 | 99.0 | 3.6 |
Cosine Decision Tree | 21.0 | 33.5 | 37.0 | 66.5 |
Medium Decision Tree | 48.0 | 66.8 | 84.0 | 33.2 |
Cubic SVM | 99.0 | 99.3 | 100.0 | 1.3 |
Author | Year | Features | Techniques | Accuracy (%) |
---|---|---|---|---|
Nataraj et al. [51] | 2011 | GIST feature | Nearest Neighbor | 97.18 |
Anderson et al. [52] | 2012 | Gaussian kernel features | SVM | 98.0 |
Dahl et al. [53] | 2013 | Sparse binary features | Neural networks and logistic regression | 86.0 |
Zhang et al. [54] | 2014 | Graph-based feature | Semantics-based | 93.0 |
Pascanu et al. [55] | 2015 | Echo state networks (ESNs) and recurrent neural networks (RNNs) for feature extraction | Logistic regression and multilayer perceptron classifier | 98.3 |
Garcia [56] | 2016 | Texture features | Random Forest | 95.0 |
Moshiri et al. [57] | 2017 | Filter-based feature | Machine learning techniques | 99.0 |
Liu et al. [58] | 2017 | N-gram-based texture feature | Shared nearest neighbor (SNN) clustering algorithm | 98.9 |
Cakir et al. [59] | 2018 | Deep learning-based feature | Gradient boosting | 96.0 |
Kalash et al. [60] | 2018 | GIST features | CNN-based architecture | 98.5 |
Naeem et al. [61] | 2019 | Local and global malware pattern (LGMP) features | SVM, KNN | 98.0 |
Cui et al. [12] | 2019 | CNN-based features | CNN | 97.6 |
Naeem et al. [62] | 2019 | Combined local and global malware (CLGM) features | DCNN | 98.18 |
This paper | 2020 | Fused SFTA and deep network features | DCNN (AlexNet, Inception v3) | 99.3 |
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Nisa, M.; Shah, J.H.; Kanwal, S.; Raza, M.; Khan, M.A.; Damaševičius, R.; Blažauskas, T. Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features. Appl. Sci. 2020, 10, 4966. https://doi.org/10.3390/app10144966
Nisa M, Shah JH, Kanwal S, Raza M, Khan MA, Damaševičius R, Blažauskas T. Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features. Applied Sciences. 2020; 10(14):4966. https://doi.org/10.3390/app10144966
Chicago/Turabian StyleNisa, Maryam, Jamal Hussain Shah, Shansa Kanwal, Mudassar Raza, Muhammad Attique Khan, Robertas Damaševičius, and Tomas Blažauskas. 2020. "Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features" Applied Sciences 10, no. 14: 4966. https://doi.org/10.3390/app10144966
APA StyleNisa, M., Shah, J. H., Kanwal, S., Raza, M., Khan, M. A., Damaševičius, R., & Blažauskas, T. (2020). Hybrid Malware Classification Method Using Segmentation-Based Fractal Texture Analysis and Deep Convolution Neural Network Features. Applied Sciences, 10(14), 4966. https://doi.org/10.3390/app10144966