论文标题
电子邮件摘要以帮助用户网络钓鱼识别
Email Summarization to Assist Users in Phishing Identification
论文作者
论文摘要
最近，网络捕捞攻击变得更加精确，针对和量身定制，仅在有特定信息或提示的情况下激活。它们的适应性比传统的网络钓鱼检测更大。因此，自动检测系统不能总是100％准确，在面对潜在的网络钓鱼电子邮件时，会增加预期行为的不确定性。另一方面，以人为中心的防御方法将重点放在用户培训上，但面临着使用户保持最新的困难。因此，以新颖的方式分析电子邮件的内容的进步，以及总结与电子邮件接收者最相关的内容是一个前瞻性门户，以进一步打击这些威胁。解决这一差距，这项工作利用基于变压器的机器学习来（i）分析潜在的心理触发因素，（ii）检测可能的恶意意图，以及（iii）创建电子邮件的代表性摘要。然后，我们将这些信息融为一体，并将其呈现给用户，以允许他们（i）轻松确定电子邮件是否为“ Phishy”和（ii）自学高级恶意模式。
Cyber-phishing attacks recently became more precise, targeted, and tailored by training data to activate only in the presence of specific information or cues. They are adaptable to a much greater extent than traditional phishing detection. Hence, automated detection systems cannot always be 100% accurate, increasing the uncertainty around expected behavior when faced with a potential phishing email. On the other hand, human-centric defence approaches focus extensively on user training but face the difficulty of keeping users up to date with continuously emerging patterns. Therefore, advances in analyzing the content of an email in novel ways along with summarizing the most pertinent content to the recipients of emails is a prospective gateway to furthering how to combat these threats. Addressing this gap, this work leverages transformer-based machine learning to (i) analyze prospective psychological triggers, to (ii) detect possible malicious intent, and (iii) create representative summaries of emails. We then amalgamate this information and present it to the user to allow them to (i) easily decide whether the email is "phishy" and (ii) self-learn advanced malicious patterns.
