摘要
随着网络攻击手段持续演进,传统恶意软件防范技术应对新型威胁存在难度。研究围绕基于人工智能(Artificial Intelligence,AI)的防范技术展开,重点关注特征码、行为分析和机器学习算法在恶意软件自动识别中的应用。借助AI技术,可提高恶意代码检测的准确性与效率,实现对已知及未知恶意软件的识别与隔离。实验设计包含数据集选择、数据预处理与特征提取环节,为AI系统的训练和评估提供了理论支撑。
As cyberattack methods continue to evolve,traditional malware prevention technologies face difficulties in dealing with new threats.The study focuses on the application of artificial intelligence(AI)based prevention technologies,with a particular emphasis on feature codes,behavior analysis,and machine learning algorithms in automatic identification of malicious software.With the help of AI technology,the accuracy and efficiency of malware detection can be improved,and the identification and isolation of known and unknown malware can be achieved.The experimental design includes data set selection,data preprocessing,and feature extraction,providing theoretical support for the training and evaluation of AI systems.
作者
于明荣
YU Mingrong(China Tobacco Jiangsu Industrial Co.,Ltd.,Nanjing Jiangsu 210000,China)
出处
《信息与电脑》
2025年第16期28-30,共3页
Information & Computer
关键词
AI
恶意软件
行为分析
机器学习
特征提取
AI
malware
behavior analysis
machine learning
feature extraction
