# A Survey of CNN-Based Network Intrusion Detection
## 7 Other Factors of CNN-Based IDS Experiments
### Hyperparameter Setting  
A model's accuracy and learning capability are substantially affected by hyperparameters, embodying higher-level characteristics that cannot be learned from the training set in the procedure of deep learning. The number of training epochs, initial weight initializations, regularization, nonlinearity, mini-batch size, optimizer, choice of objective function, and the learning rate of the model are among the mentioned hyperparameters [24].  
Furthermore, practical experience and professional knowledge are needed for the search for optimal topological architectures. Overall, the performance of the model is hypothetically enhanced by increasing the number of neurons and deepening the network depths of the fully connected layer. Nonetheless, the performance of the model diminishes after a certain level and depth are reached in the CNN [52].  
Manual search (trial and error) and grid search are the commonly used strategies for setting hyperparameters. However, limited efficacy is associated with these techniques. Significant computing resources are needed in grid searches, which can incur exponential increases in trials among the set of trials established by formulating each potential combination of hyperparameter values. Meanwhile, reproducing results is challenging for the manual search technique [24]. Efficacious searches over large configuration spaces are enabled by the enhanced model parameters that the random-search strategy provides, thereby making the most of the limited computational budget in comparison to grid search. Manual search was utilized in multiple papers reviewed in this survey. Additionally, the implementation of the random-search strategy was also carried out for hyperparameter optimization [24].  
The establishment of optimal topological architectures and hyperparameters is enabled by metaheuristic algorithms that belong to the larger class of evolutionary algorithms (EAs). The multi-objective evolutionary algorithm based on decomposition (MOEA/D), lion swarm optimization (LSO), genetic algorithm (GA), and ant lion optimizer (ALO) are examples of said algorithms utilized in CNN-based IDS.  
Hyperparameter selection and the CNN structure are enabled via the utilization of the GA in academic work [3]. In another work, a high classification rate and minimum error rate were obtained via the use of ALO [54]. The tuning of hyperparameters was performed through the use of LSO in another study [61].  
A CNN model was built automatically through the MOEA/D in another investigation. A chromosome of MOEAs is the outcome of the transformation of the parameters. The hyperparameter group, the fully connected group, and the convolution group are the three groups into which parameters are separated as per their distinct roles. The dropout rate, the total number of neurons in every fully connected layer, and the total number of fully connected layers are included within the fully connected group. The learning rate, the learning rule, and the batch size are included within the hyperparameter group. In this approach, the model complexity, which is the number of parameters of the CNN, is the first objective, and the detection error rate is the second objective [52].