## 5 Securing CPS
### Approach  
We used two datasets for experiment purposes; one is the malicious user dataset while the other is the KDD cup 1999 dataset [40]. URL reputation is treated as a binary classification problem where positive examples present malicious URLs and negative examples present benign URLs. It consists of 2.4 million examples of URLs while the number of features is 3.2 million. The second dataset is KDD Cup 1999 Data [41] that was used in the competition of Knowledge discovery and data mining tool for intrusion detection in a network. In the case of attack or intrusion, the connection is considered as 'bad' otherwise 'good'; data simulation is performed in a military environment. NSL-KDD [42] is also widely used for evaluating intrusion detection models. Each record of intrusion has symbol features (three-dimensional) and digital features (42-dimensional). The labels are mainly divided into normal, DoS, Prob, U2R, and R2L types of attacks. It contains a total of 125,973 samples in the train set and 22,544 samples in the test set. MLP is used to perform classification tasks, which is a feed-forward neural network consisting of the input layer, hidden layer, and output layer. Each neuron of the input layer represents the feature; hidden layer neurons process the data and store weights during the training phase and neurons of the output layer represent the output variable. The number of nodes or neurons in the input layer is the same as the number of features feeds to the neural network layer and the number of nodes or neurons in the output layer represents the number of target classes. The number of nodes in the hidden layer is an architectural issue, and the main focus is to generalize it and optimize it with the appropriate number of parameters for the classification task. MLP works on backpropagation that is based on the gradient descent method. Figure 4 presents the architecture of MLP. It has been used in various classification tasks. Hyperparameters of the deep learning model include epochs, number of layers, and batch size. Considering limited computing power and the training time, the model is kept simple, having only three layers with a batch size of 32, and Relu is used as an activation function. We utilized 80% data for training and 20% for testing the model. Figure 5 presents the experimental results of MLP in terms of accuracy, precision, recall, and F1 score. Results reveal that a simple deep learning-based model is very effective in predicting malicious URLs and intrusion detection. Having low computational power in a wireless network, a simple deep learning model is showing robust results with 99.62% for the malicious URL detection dataset and 99.87% for intrusion detection on the KDD Cup 99 dataset. Table 3 shows the classification results of the proposed approach and other methods in the literature [34; 40; 41]. The proposed MLP outperforms other methods in terms of all evaluation measures for both datasets. In the detection of malicious URLs, the proposed approach exceeds the results proposed in [40] using SVM. [PERSON] et al. [34] proposed feature reduction based on correlation by combining recursive feature elimination with a decision tree, but the results are still lower than the proposed approach on the KDD Cup 99 dataset. Overall, the proposed method has shown better performance in KDD Cup 99 and malicious URL detection datasets. Figure 4: Architecture of MLP used for experiments. Table 4 shows 10-fold results to show the generalizability and consistency of the proposed model. Table 5 shows the performance comparison of the proposed approach with existing techniques using KDD-CUP and NSL-KDD datasets. It can be observed that the proposed can provide higher accuracy regarding different kinds of attacks like DoS, R2L, etc.