深度学习在网络安全防御技术中的研究与应用

Research and Application of Deep Learning in Network Security Defense Technology

当前,网络攻击的形式日趋复杂和多样化,传统的网络安全防御措施已难以应对新出现的网络威胁。深度学习作为一种先进的机器学习方法,在图像识别、自然语言处理等领域展现出了卓越的性能,其强大的模式识别和自动化特征提取能力为网络安全防御提供了新的可能性。本文研究了深度学习在网络安全防御领域的应用现状,重点阐述了基于深度学习的入侵检测系统、恶意代码检测、网络流量分析等核心技术,探讨了深度学习在网络安全防御领域所面临的挑战与未来发展的方向。

As cyber attacks become increasingly complex and diverse,traditional cybersecurity defense measures are no longer able to cope with emerging cyber threats.As an advanced machine learning method,deep learning has demonstrated outstanding performance in areas such as image recognition and natural language processing.Its powerful pattern recognition and automated feature extraction capabilities provide new possibilities for cybersecurity defense.This paper studies the current status of deep learning applications in the field of cybersecurity defense,focusing on core technologies such as intrusion detection systems,malicious code detection,and network traffic analysis based on deep learning,and explores the challenges and future development directions of deep learning in the field of cybersecurity defense.