Research Article
KTSDroid: A Framework for Android Malware Categorization Using the Kernel Task Structure
Table 2
Related work on malware detection using kernel task structure features.
| Study | Number of effective features | Feature selection techniques | Algorithm classification | Reported performance | Data set size | Multiclass classification | KTS category analysis |
| Wang and Li [12] | 10–40 out of 112 | PCA, correlation, IG, and chi-square | Naïve Bayes, decision trees, and neural network | ACC: 94%–98% | 1275 malware, 1275 benign | | Partial | Alawneh et al. [14] | 43 out of 112 | Logistic regression | Neural network | ACC: 96.80% | 1200 malware, 1200 benign | | | Shahzad et al. [15] | 32 out of 90 | Correlation | Decision trees (J48) | ACC: 93%–96% | 110 malicious, 110 benign | | | Kim and Choi [20] | 36 out of 59 | Manual | Support vector machines (SVM) | ACC: 98.85%, | 6 malicious | | |
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