Research Article
Towards the Development of a Cloud Computing Intrusion Detection Framework Using an Ensemble Hybrid Feature Selection Approach
Table 13
Comparison with related work.
| Source | FS method (type) | ML algorithm (s) | CA | ER | PR | RC | FPR | FNR | FAR (FNR + FPR)/2 |
| Proposed binary detection engine using HEFSM | Hybrid | HEFSM (DT, RF, KNN) | 99.55 | 0.45 | 99.52 | 99.54 | 0.44 | 0.46 | 0.45 | Luo and Xia [38] | Filter | SVM | 94.36 | — | — | — | — | — | — | Eesa et al. [40] | Wrapper | DT | 91.99 | — | — | 91 | 3.917 | — | — | Bhattacharya and Sel-vakumar [51] | Hybrid | BN, J48, KM | 96.46 | — | — | — | — | — | — | Osanaiye et al. [1] | Filter | DT | 99.67 | — | — | 99.76 | 0.42 | — | — | Ambusaidi et al. [37] | Filter | SVM | 99.91 | — | — | 98.76 | 0.28 | — | — | Belouch et al. [44] | Wrapper | REPTree | 89.85 | — | — | — | — | — | — | Moustafa and Slay [47] | Hybrid | EM, LR, NB | 82.1 | — | — | — | — | — | 17.5 | Mogal et al. [48] | Hybrid | NB, LR | 99.82 | — | — | — | — | — | — | Idhammad et al. [39] | Filter | ANN | 99.2 | — | — | — | — | — | 0.02 | Vijayanand et al. [45] | Wrapper | SVM | 98.95 | — | — | — | 4.1 | 18.5 | 11.3 | Aljawarneh et al. [46] | Wrapper | NB, J48, RT | 99.81 | — | — | — | — | — | — | Anwer et al. [50] | Hybrid | J48, NB | 88 | — | — | — | — | — | — | Sun et al. [41] | Wrapper | SVM | 91.68 | — | — | — | — | — | — | Pham et al. [42] | Wrapper | EC (J48) | 84.25 | — | — | — | 2.79 | — | — | Besharati et al. [43] | Wrapper | EC (DT, LDA, ANN) | 97.51 | — | — | — | — | — | — | Mohammadi et al. [49] | Hybrid | DT | 95.03 | — | — | 95.23 | 1.65 | — | — | Maza and Touahria [15] | Hybrid | NB, MLP, SVM, KNN, DT | 99.11 | — | — | — | — | — | — | Tama et al. [22] | Hybrid | RoF, CF | 85.8 | — | — | 88 | — | — | — |
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DT: Decision Tree; SVM: support vector machine; REPTree: reduced error pruning tree algorithm; EM: expectation-maximisation clustering; LR: logistic regression; BN: bayesian networks; NB: Naïve Bayes; KM: K means learning algorithm; ANN: artificial neural networks; J48: J48 decision tree; EC: ensemble classifiers, bagging, or boosting; CF: conjunctive rule; LDA: linear discriminant analysis; MLP: multilayer perceptron; RoF: rotation forest.
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