The problem of intrusion detection has new solutions, thanks to the widespread use of machine learning in the field of network security, but it still has a few issues at this time. Traditional machine learning techniques to intrusion detection rely on expert experience to choose features, and deep learning approaches have a low detection efficiency. In this paper, an intrusion detection model based on feature selection and improved one-dimensional convolutional neural network was proposed. This model first used the extreme gradient boosting decision tree (XGboost) algorithm to sort the preprocessed data, and then it used comparison to weed out 55 features with a higher contribution. Then, the extracted features were fed into the improved one-dimensional convolutional neural network (I1DCNN), and this network training was used to complete the final classification task. The feature selection and improved one-dimensional convolutional neural network (FS-I1DCNN) intrusion detection model not only solved the traditional machine learning method of relying on expert experience to extract features but also improved the detection efficiency of the model, reduced the training time while reducing the dimension, and increased the overall accuracy. In comparison to the I1DCNN model without feature extraction and the conventional one-dimensional convolutional neural network (1DCNN) model, the experimental results demonstrate that the FS-I1DCNN model’s overall accuracy increases by 0.67% and 2.94%, respectively. Its accuracy, precision, recall, and $F1$-score were significantly better than those of the other intrusion detection models, including SVM and DBN.

The security issues with mobile devices have received more attention as a result of the development of mobile Internet technology and the adoption of mobile intelligent terminal devices. It is becoming more crucial to quickly and effectively identify and remove harmful applications from systems in order to protect user data and personal devices. The Dalvik bytecode, permission applications, and system calls of Android apps are the main targets of the current Android malware analysis approaches. However, in recent years, an increasing amount of Android malware conceals harmful code in native code. The method for using program gene technology to identify malware on the Android platform is presented in this research. This method extracts executable library files from the Native layer binary executable files of Android programs and disassembles the library files to obtain program genes. Then, the programs genes perform feature screening by the information gain method and next use Word2Vec to express the semantic abstraction of the screened features. Finally, the screened features were used in deep neural network models for training and detection. The experimental results demonstrate that compared with KNN, SVM, and other machine learning algorithms, the deep neural network model is more effective and the detection accuracy reaches up to 97.51. Thus, it confirmed the feasibility of the Android malicious program detection method based on program genes in this paper.