# FCNN-SE: An Intrusion Detection Model Based on a Fusion CNN and Stacked Ensemble
## 2 Related Work
To improve the intrusion detection accuracy further while improving the classification performance, a large number of methods for feature selection and optimization have been proposed and verified on different datasets. [PERSON] et al. [38] proposed a feature selection method based on multilevel correlation, which selects important features and reduces the training set size based on the multilevel correlation between the features; the verification results have indicated that this method can successfully improve detection capability. [PERSON] et al. [39] used feature selection methods, such as relevance-based feature subset selection, chi-square attribute evaluation, gain ratio attribute evaluation, and information gain attribute evaluation, to improve the data quality. The results have indicated that by adopting a feature selection strategy, considerable acceptable attack detection accuracy can be obtained at minimal system overhead. [PERSON] et al. [40] designed a feature extraction method named the GRASP-FS, which uses the F1 score as a fitness function for adapting the greedy random adaptive search process (GRASP) meta-heuristic. The experiments on the SWaT dataset showed that the GRASP-FS constructed a simplified subset of five features from 51 available features and used random trees as classifiers, achieving the F1 score and accuracy of 96.97% and a 99.65%, respectively. All the above-mentioned feature extraction methods can reduce the feature space size of the original dataset and improve the detection performance of a classifier to a certain extent. However, high-dimensional feature information cannot be extracted from a dataset using feature extraction methods that only reduce the feature space size. Recently, a large number of evaluation metrics based on statistical principles have been applied in the field of intrusion detection to evaluate the performances of intrusion detection systems. [PERSON] et al. [41] used the DT, LR, KNN, SVM, and Bi-LSTM as base learners and adopted the voting mechanism for classification. The weights of all base learners were optimized using a hybrid approach of particle swarm optimization and a modified salps swarm algorithm. The proposed ensemble classifier achieved good performances in terms of accuracy rate, attack detection rate, and false alarm rate. [PERSON] et al. [42] proposed a bat-inspired optimization and correlation-based feature selection (BIOCFS) algorithm, which can estimate the correlation between identified features and select optimal subsets for the training and testing phases. In the BIOCFS algorithm, the base learner in the ensemble classifier uses the forest by penalizing attributes, random forest, and C4.5. The BIOCFS algorithm has an excellent performance in handling multiclassification and unbalanced datasets. On the NSL-KDD dataset, the BIOCFS algorithm can achieve a maximum classification accuracy, precision, F1 score, and attack detection ratio of 0.994, 0.993, 0.992, and 0.992, respectively, and the minimum false alarm rate of 0.008%. [PERSON] et al. [43] proposed a multi-granularity feature generation algorithm, which converts features into discrete features with different numbers of classes, where different numbers of classes indicate different granularities. On the KDD99 dataset, the multi-granularity feature generation algorithm can achieve the detection rates of 100%, 100%, and 99.43% for two-, five-, and multi-class tasks, respectively; on the NSL-KDD dataset, this algorithm can achieve the detection rates of 100%, 100%, and 90.84% for the two-, five-, and multi-class tasks, respectively. All aforementioned intrusion detection models can achieve excellent detection results, but their comprehensive performance cannot be fully evaluated using a single evaluation metric. In this paper, an intrusion detection model (FCNN-SE) based on the FCNN and SE, which can effectively detect attacks in networks with a high traffic data rate, is proposed.