Phishing email attacks are among the most common and most harmful
cybersecurity attacks. With the emergence of generative AI, phishing attacks
can be based on emails generated automatically, making it more difficult to
detect them. That is, instead of a single email format sent to a large number
of recipients, generative AI can be used to send each potential victim a
different email, making it more difficult for cybersecurity systems to identify
the scam email before it reaches the recipient. Here we describe a corpus of
AI-generated phishing emails. We also use different machine learning tools to
test the ability of automatic text analysis to identify AI-generated phishing
emails. The results are encouraging, and show that machine learning tools can
identify an AI-generated phishing email with high accuracy compared to regular
emails or human-generated scam email. By applying descriptive analytic, the
specific differences between AI-generated emails and manually crafted scam
emails are profiled, and show that AI-generated emails are different in their
style from human-generated phishing email scams. Therefore, automatic
identification tools can be used as a warning for the user. The paper also
describes the corpus of AI-generated phishing emails that is made open to the
public, and can be used for consequent studies. While the ability of machine
learning to detect AI-generated phishing email is encouraging, AI-generated
phishing emails are different from regular phishing emails, and therefore it is
important to train machine learning systems also with AI-generated emails in
order to repel future phishing attacks that are powered by generative AI.

通过描述一个开放给公众使用的 AI 生成钓鱼邮件语料库，论文表明通过使用自动文本分析的机器学习工具，可以高准确率地识别基于 AI 生成的钓鱼邮件，并且 AI 生成的钓鱼邮件与常规钓鱼邮件不同，因此有必要在训练机器学习系统时使用 AI 生成的邮件，以防止由生成型 AI 支持的未来钓鱼攻击。

基于人工智能的钓鱼邮件攻击分析与预防

Analysis and prevention of AI-based phishing email attacks

Combining computational technologies and humanities is an ongoing effort
aimed at making resources such as texts, images, audio, video, and other
artifacts digitally available, searchable, and analyzable. In recent years,
deep neural networks (DNN) dominate the field of automatic text analysis and
natural language processing (NLP), in some cases presenting a super-human
performance. DNNs are the state-of-the-art machine learning algorithms solving
many NLP tasks that are relevant for Digital Humanities (DH) research, such as
spell checking, language detection, entity extraction, author detection,
question answering, and other tasks. These supervised algorithms learn patterns
from a large number of "right" and "wrong" examples and apply them to new
examples. However, using DNNs for analyzing the text resources in DH research
presents two main challenges: (un)availability of training data and a need for
domain adaptation. This paper explores these challenges by analyzing multiple
use-cases of DH studies in recent literature and their possible solutions and
lays out a practical decision model for DH experts for when and how to choose
the appropriate deep learning approaches for their research. Moreover, in this
paper, we aim to raise awareness of the benefits of utilizing deep learning
models in the DH community.

将计算技术与人文学科相结合是一项不断努力的工作，旨在使文本、图像、音频、视频和其他人工制品等资源能够以数字化形式进行存储、检索和分析。本文分析最近文献中多种人文学科研究的使用案例，探讨了人文学科研究中采用深度学习方法面临的训练数据和领域适应性方面的挑战，并提出了实用的决策模型，以指导人文学科专家在何时以及如何选择合适的深度学习方法进行研究。此外，本文旨在提高人文学科社区对于利用深度学习模型的收益的认知。