Prediction and Analysis of Malicious Software Infection in Mobile Computers
With the rapid development of mobile computing technology,mobile devices such as smartphones and tablets have become an indispensable part of people's daily lives,as a result,they have become new targets for malware attacks.This study proposes a deep learning based malware infection prediction model,which uses convolutional neural networks(CNN)for feature extraction and prediction of malware.The research methods include data preprocessing,model training and validation,and evaluation of results.The dataset is sourced from publicly available malware repositories and actual infection data from cybersecurity companies.The experimental results show that the model performs well in multiple indicators such as accuracy,recall,precision,and F1 score,demonstrating its potential in processing large-scale datasets and identifying new types of malware.The research results not only improve the accuracy of mobile computer malware prediction,but also provide important contributions to the theory and practice of mobile computing security.
mobile computersmalicious softwarepredictive analysisdeep learningConvolutional Neural Network(CNN)