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移动式计算机中恶意软件感染预测分析

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随着移动计算技术的快速发展,智能手机和平板电脑等移动设备已成为人们日常生活中不可或缺的一部分,也因此成为恶意软件攻击的新目标.本研究提出一种基于深度学习的恶意软件感染预测模型,采用卷积神经网络(CNN)进行恶意软件的特征提取和预测.研究方法包括数据预处理、模型训练和验证以及结果的评估.数据集来源于公开恶意软件库和网络安全公司的实际感染数据.实验结果表明,该模型在准确率、召回率、精确率和F1得分等多个指标上表现良好,显示出在处理大规模数据集和识别新型恶意软件方面的潜力.研究成果不仅提升了移动式计算机恶意软件预测的准确性,也为移动计算安全领域的理论和实践提供了重要贡献.
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)

彭秋华

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湖南省澧县职业中专学校,湖南常德 415500

移动式计算机 恶意软件 预测分析 深度学习 卷积神经网络(CNN)

2024

软件
中国电子学会 天津电子学会

软件

影响因子:1.51
ISSN:1003-6970
年,卷(期):2024.45(2)
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