Table 13.
Results of the proposed system against existing security systems using the Drebin dataset.
| Reference | Year | Datasets | Model | Accuracy (%) |
|---|---|---|---|---|
| Ref. [72] | 2021 | Drebin | CNN | 91 |
| Ref. [73] | 2018 | Drebin | RF, J.48, NB, Simple Logistic, BayesNet TAN, BayesNet K2, SMO PolyKernel, IBK, SMO NPolyKernel | 88–96 |
| Ref. [74] | 2021 | Drebin | CBR, SVM, DT | 95 |
| Ref. [75] | 2019 | Drebin | Random forest tree | 96.7 |
| Ref. [76] | 2018 | Drebin | DT | 97.7 |
| Ref. [77] | 2019 | Drebin | RF with 1000 decision trees | 98.7 |
| Ref. [78] | 2019 | Drebin | SVM | 93.7 |
| Ref. [79] | 2019 | Drebin | Random forest tree | 94 |
| Ref. [80] | 2019 | Drebin | Random forest tree | 96 |
| Ref. [81] | 2016 | Drebin | Random forest tree | 97 |
| Ref. [82] | 2021 | Drebin | CNN | 98.2 |
| Proposed model | 2022 | Drebin | LSTM CNN-LSTM |
99.40 97.82 |