基于深度学习的恶意软件检测与防御策略研究

Research on Malicious Software Detection and Defense Strategy Based on Deep Learning

随着信息技术的快速发展,恶意软件逐渐成为威胁网络安全的主要因素.深度学习技术是一种先进的机器学习方法,在多个领域均具有突出的性能.文中探讨了深度学习技术在恶意软件检测和防御中的应用,提出了一种基于深度学习的恶意软件检测与防御框架.实验证明,该框架能有效提高恶意软件的识别准确率,并在防御策略中展示出了良好的实时响应能力.结果表明,深度学习技术在提升恶意软件检测与防御效果方面具有显著的优势.

With the rapid development of information technology,malicious software has gradually become the main factor threatening cyber security.Deep learning technology is an advanced machine learning method with outstanding performance in many fields.This paper discusses the application of deep learning technology in malicious software detection and defense,and proposes a malicious software detection and defense framework based on deep learning.Experiments show that the framework can effectively improve the recognition accuracy of malicious software and demonstrate good real-time response ability in defense strategies.The results show that deep learning technology has significant advantages in improving the detection and defense effect of malicious software.