# Classification of Malicious URLs by CNN Model Based on Genetic Algorithm
###### Abstract
[13] proposed a detection method based entirely on lexical features. The convolutional neural network extracts and classifies the URL strings and finally obtains a better classification result. [PERSON] et al. [14] proposed a joint neural network algorithm model of Bi-IndRNN and CapsNet to identify and detect malicious URLs, extract vector features and texture features at the character and word levels, and perform feature fusion. Classification by a joint neural network effectively improves the efficiency and accuracy of malicious URL detection. [PERSON] et al. [15] proposed a deep neural network based on CNN for URLNet for malicious URL detection. For manual feature engineering and the inability to handle features that are not visible in the test URL, the characters cnn and Word cnn are proposed, and the network is jointly optimized. In addition, many researchers also identify URLs from the aspects of extracting texture features and structural features. [PERSON] et al. [16] proposed a parallel neural joint model algorithm for the analysis and detection of malicious URLs, using CapsNet to combine texture features with text features and capture multi-modal vectors. [PERSON] et al. [17] proposed a decision tree intelligent detection method for two-dimensional code URLs. The accuracy rate in identifying regular URLs and two-dimensional code malicious URLs has a relatively good effect, but the selected data volume is low, and the model's generic ability is not well trained. [PERSON] et al. [18] proposed a statistical method to study the character features of malicious URLs and obtain the character distribution features and structural features. The extracted features were combined with random forests to obtain better performance than the original features, and the parameters of random forests were adjusted to be the best. [PERSON] et al. [19] proposed an efficient method for detectingmalicious URLs based on segment pattern, which parses the three semantic segments of the domain name, path name, and file name in the marked malicious URL and quickly calculates the pattern of each semantic segment of the malicious URL through the inverted index of the triple as the term, and finally determines the segment pattern according to the inverted index. There is no research on other features, such as IP address for the content of domain name, path, and file name. [PERSON] et al. [20] proposed a system for analyzing URLs in network traffic. Each correctly classified URL is reused as part of a new data set. Different clustering techniques are used to identify missing features on malicious URLs. Through the OSC perceptron optimization algorithm, the final recognition accuracy is improved, and the overall detection rate is increased to 82.9%. Based on the above research background, in order to better identify malicious URLs and improve the accuracy of detection, there are some unobvious features in the URL text. Based on the grammatical features, structural features, and probability features of URLs, a convolutional neural network model based on genetic algorithm optimization is proposed. High-dimensional feature space, on the one hand, will consume a lot of training time; on the other hand, there may be some interference information on the training results. Therefore, firstly, the genetic algorithm is used to reduce the dimension of URL features, and the feature subset with a good classification effect is searched. Secondly, the feature subset is classified by a convolutional neural network. Finally, compared with the traditional machine learning method, the accuracy of malicious URL recognition is improved.