Table 2.
Comparative analysis of the proposed Model with existing analogues.
| Analytical Modeling of MC | Criteria | Score | |||||
|---|---|---|---|---|---|---|---|
| A | B | C | D | E | F | ||
| 1. Malware detection by data mining techniques based on positionally dependent features [49] |
+/− | + | +/− | +/− | + | +/− | 4 |
| 2. Fine-Grained Compiler Identification With Sequence-Oriented Neural Modeling [50] |
+ | + | − | + | + | +/− | 4.5 |
| 3. Function Identification in Android Binaries With Deep Learning [51] | − | + | − | +/− | + | + | 3.5 |
| 4. Polymorphic Malware Detection Using Hierarchical Hidden Markov Model [52] |
+/− | − | + | − | − | − | 1.5 |
| 5. Feature Extraction Method for Cross-Architecture Binary Vulnerability Detection [53] |
+ | +/− | +/− | − | +/− | +/− | 3 |
| 6. Vision-Based Malware Detection: A Transfer Learning Approach Using Optimal ECOC-SVM Configuration [13] |
− | + | − | − | − | +/− | 1.5 |
| 7. Cross-Architecture Intemet-of-Things Malware Detection Based on Graph Neural Network [54] |
+ | + | +/− | + | +/− | − | 4 |
| 8. Asteria: Deep Learning-based AST-Encoding for Cross-platform Binary Code Similarity Detection [55] |
+ | − | +/− | +/− | +/− | − | 2.5 |
| Proposed Model | + | + | + | + | + | + | 6 |
The following designations and points were used: “+”—full compliance with the criterion (1 point); “+/−”—partial compliance with the criterion (0.5 points); “−”—failure to meet the criterion. Criterion (0 points).