使用深度学习技术的入侵检测

Syeda Fareeha Batool, Faisal Rehman, Hana Sharif, Maheen Jaffer, Anza Gul, Sameen Butt
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引用次数: 0

摘要

所有公司都将入侵检测系统作为其网络安全程序的基本要素。随着越来越多的信息可以在互联网上以数字形式访问,对强大的网络安全措施的需求日益增长,以防止数据泄露和恶意软件攻击。自动化入侵检测系统需要跟上不断增加的攻击和新的恶意软件品种的步伐。计算机视觉、自然语言处理和语音识别都是深度学习方法应用的例子,这些方法目前正在网络安全领域中使用。本研究对23篇在入侵检测系统中使用深度学习的论文进行了深入分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intrusion Detection using Deep Learning Techniques
All firms use intrusion detection systems as a fundamental element of their cyber security procedures. As more and more information become accessible in digital form on the internet, the demand for robust cyber security measures to protect against data breaches and malware assaults has grown. Automated intrusion detection systems are needed to keep pace with the ever-increasing number of assaults and new malware varieties. Computer vision, natural language processing, and voice recognition are all examples of applications of Deep Learning methods that are currently being used in the realm of cyber security. An in-depth analysis of twenty-three papers using Deep Learning in intrusion detection systems is performed in this study.
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