启发式机器学习方法用于识别网络和电子邮件平台上的网络鱼威胁
Ramprasath Jayaprakash1, Krishnaraj Natarajan2, J Alfred Daniel3
1Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, India.
Frontiers in artificial intelligence
|November 5, 2024
概括
本研究介绍了一种新的基于启发式的机器学习技术,用于检测跨URL,电子邮件和网站的网络鱼攻击. 该方法实现了高精度,大大提高了在线安全,防止数据盗窃和诈骗.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 网络鱼攻击构成了重大威胁,旨在通过欺骗性的通信方法窃取密码和财务数据等敏感信息.
- 这些攻击通常涉及嵌入在URL,电子邮件或网站中的恶意程序,欺骗用户透露机密细节.
- 现有的安全措施需要加强,以有效打击网络鱼计划不断变化的性质.
研究的目的:
- 开发和评估一种新的基于启发式的机器学习技术,用于检测网络鱼企图.
- 通过准确识别恶意URL,电子邮件和网站来提高在线通信的安全性.
- 提供强大的防御机制,防止各种形式的网络鱼和保护用户数据.
主要方法:
- 在收集的数据集上进行了数据预处理,包括数据清理和属性选择.
- 基于启发式的机器学习算法被用来识别URL,电子邮件和网站中的网络鱼模式.
- 该技术使用56个URL分析功能和48个网站分析功能进行了测试.
主要成果:
- 提出的技术在检测基于URL的网络鱼攻击方面表现出很高的准确性,达到97.2%的效果.
- 使用开发的方法,电子邮件网络鱼检测产生了97.4%的卓越准确率.
- 基于网站的网络鱼检测实现了令人印象深刻的98.1%的准确性.
结论:
- 基于启发式的机器学习方法为识别和减轻网络鱼攻击提供了非常有效的解决方案.
- 拟议的技术显著提高了对各种在线平台数据盗窃的安全性.
- 准确检测网络鱼企图对于保护用户在数字时代至关重要.
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