利用数据分析,通过机器学习和深度学习彻底改变网络安全
Asadi Srinivasulu1,2, Tae-Hoon Kim3, Ravikumar Chinthaginjala4
1Cooperative Research centre for contamination Assessment and Remediation of the Environment (CRC CARE),Global Centre for Environmental Remediation/College of Engineering Science & Environment, ATC Building, The University of New Castle, Callaghan, NSW2308, Australia. srinuasadi@gmail.com.
Scientific reports
|August 29, 2025
概括
这项研究使用卷积神经网络 (CNN) 分析网络安全数据,在检测和分类威胁方面实现高准确性. 这种深度学习方法可以增强网络安全防御,
科学领域:
- 网络安全
- 机器学习
- 数据科学
背景情况:
- 数字技术的发展导致了复杂的网络攻击.
- 强有力的网络安全措施至关重要.
- 传统的方法需要改进.
研究的目的:
- 探索卷积神经网络 (CNN) 进行网络安全数据分析.
- 评估CNN的准确和有效的威胁检测和分类.
- 研究深度学习与网络安全的整合.
主要方法:
- 使用卷积神经网络 (CNN) 作为主要技术.
- 设计了一个CNN架构,
- 产生了代表网络安全事件的合成数据.
主要成果:
- 在识别和分类网络威胁方面,
- 该模型有效地捕获了网络安全数据中的复杂模式.
- 在加强网络安全防卫方面,
结论:
- 深度学习技术,特别是CNN,可以补充传统的网络安全.
- 基于CNN的数据分析为主动威胁检测提供了基础.
- 未来的工作应该包括用于验证的现实数据.
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