在网络和电子邮件平台上识别网络钓鱼威胁的启发式机器学习方法。

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-10-21 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1414122
Ramprasath Jayaprakash, Krishnaraj Natarajan, J Alfred Daniel, Chandru Vignesh Chinnappan, Jayant Giri, Hong Qin, Saurav Mallik
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引用次数: 0

摘要

在这个竞争残酷的世界里,先进技术时代让生活变得更加舒适。然而,也有一些新兴的有害技术构成了威胁。毫无疑问,网络钓鱼是日益受到关注的问题之一,它通过通信劫持技术从任何目标节点窃取密码、安全代码和个人数据等重要信息。此外,网络钓鱼攻击还包括发送源自可信来源的虚假信息。此外,网络钓鱼攻击的目的是让受害者运行恶意程序并泄露机密数据,如银行凭证、一次性密码和用户登录凭证。其唯一目的就是通过嵌入在 URL、电子邮件和网站中的恶意程序来收集个人信息。值得注意的是,这项建议的技术可以检测到基于 URL、电子邮件和网站的网络钓鱼攻击,这将使我们受益匪浅,并确保我们免受诈骗企图的侵害。随后,利用数据清理、属性选择和机器学习技术检测攻击,对数据进行预处理以识别网络钓鱼攻击。此外,所提出的技术使用基于启发式的机器学习来识别网络钓鱼攻击。实验结果表明,拟议技术的准确率高达 97.2%。此外,针对电子邮件网络钓鱼检测提出的技术获得了 97.4% 的较高准确率。此外,针对网站网络钓鱼检测的建议技术的准确率为 98.1%,其中使用了 48 个特征进行分析。
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Heuristic machine learning approaches for identifying phishing threats across web and email platforms.

Life has become more comfortable in the era of advanced technology in this cutthroat competitive world. However, there are also emerging harmful technologies that pose a threat. Without a doubt, phishing is one of the rising concerns that leads to stealing vital information such as passwords, security codes, and personal data from any target node through communication hijacking techniques. In addition, phishing attacks include delivering false messages that originate from a trusted source. Moreover, a phishing attack aims to get the victim to run malicious programs and reveal confidential data, such as bank credentials, one-time passwords, and user login credentials. The sole intention is to collect personal information through malicious program-based attempts embedded in URLs, emails, and website-based attempts. Notably, this proposed technique detects URL, email, and website-based phishing attacks, which will be beneficial and secure us from scam attempts. Subsequently, the data are pre-processed to identify phishing attacks using data cleaning, attribute selection, and attacks detected using machine learning techniques. Furthermore, the proposed techniques use heuristic-based machine learning to identify phishing attacks. Admittedly, 56 features are used to analyze URL phishing findings, and experimental results show that the proposed technique has a better accuracy of 97.2%. Above all, the proposed techniques for email phishing detection obtain a higher accuracy of 97.4%. In addition, the proposed technique for website phishing detection has a better accuracy of 98.1%, and 48 features are used for analysis.

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来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
期刊最新文献
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