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Comparison of existing algorithms
| Author(s) | Dataset Used | Algorithm | Efficiency | IT / IPC Detected |
|---|---|---|---|---|
| Alami & Elbeqqali (2015) [19] | Microblog data | Text mining + SVM | Not detailed | No |
| Mbaziira & Jones (2016) [20] | Deceptive cybercrime text | Linguistics + ML | Medium | No |
| Kumari etal. (2018) [21] | Labeled text samples | NLTK, Scikit-learn | Moderate | No |
| Andleeb etal. (2019) [22] | MySpace bullying texts | Text mining + ML | Not detailed | No |
| Ch etal. (2020) [23] | State-wise crime stats | SVM, Decision Tree | Good | No |
| K. veena et al. (2022) [24] | Cybercrime reports | SVM | High | Potential |
| Pandey etal. (2022) [25] | Custom labeled reports | Ensemble (RF, NB, etc.) | High (noted) | No |
Section-based cybercrime classification result analysis
| Section | Algo | Precision | Recall | F1-Score | Support |
|---|---|---|---|---|---|
| 66E | RF | 1.000 | 1.000 | 1.000 | 47 |
| GB | 1.000 | 1.000 | 1.000 | ||
| ECS | 1.000 | 1.000 | 1.000 | ||
| 72A | RF | 0.700 | 0.903 | 0.789 | 31 |
| GB | 0.667 | 0.903 | 0.767 | ||
| ECS | 0.700 | 0.903 | 0.789 | ||
| 43A | RF | 0.927 | 0.760 | 0.835 | 50 |
| GB | 0.923 | 0.720 | 0.809 | ||
| ECS | 0.927 | 0.760 | 0.835 |
Performance comparison of models
| Algo | Accuracy | Precision | Recall | F1-Score | AUC |
|---|---|---|---|---|---|
| RF | 0.84 | 0.84 | 0.84 | 0.84 | 0.94 |
| GB | 0.84 | 0.83 | 0.84 | 0.85 | 0.93 |
| ECS | 0.86 | 0.86 | 0.86 | 0.86 | 0.94 |