Phishing email is a serious cyber threat that tries to deceive users by
sending false emails with the intention of stealing confidential information or
causing financial harm. Attackers, often posing as trustworthy entities,
exploit technological advancements and sophistication to make detection and
prevention of phishing more challenging. Despite extensive academic research,
phishing detection remains an ongoing and formidable challenge in the
cybersecurity landscape. Large Language Models (LLMs) and Masked Language
Models (MLMs) possess immense potential to offer innovative solutions to
address long-standing challenges. In this research paper, we present an
optimized, fine-tuned transformer-based DistilBERT model designed for the
detection of phishing emails. In the detection process, we work with a phishing
email dataset and utilize the preprocessing techniques to clean and solve the
imbalance class issues. Through our experiments, we found that our model
effectively achieves high accuracy, demonstrating its capability to perform
well. Finally, we demonstrate our fine-tuned model using Explainable-AI (XAI)
techniques such as Local Interpretable Model-Agnostic Explanations (LIME) and
Transformer Interpret to explain how our model makes predictions in the context
of text classification for phishing emails.

本研究论文介绍了一种优化的、经过微调的基于 Transformer 的 DistilBERT 模型，用于检测钓鱼邮件。通过实验，我们发现我们的模型能够有效地实现高准确性，并使用可解释的 AI 技术来解释我们的模型在钓鱼邮件的文本分类中进行预测的方式。

可解释的基于 Transformer 的网络钓鱼邮件检测模型：一种大型语言模型方法

An Explainable Transformer-based Model for Phishing Email Detection: A  Large Language Model Approach

Anomaly detection in command shell sessions is a critical aspect of computer
security. Recent advances in deep learning and natural language processing,
particularly transformer-based models, have shown great promise for addressing
complex security challenges. In this paper, we implement a comprehensive
approach to detect anomalies in Unix shell sessions using a pretrained
DistilBERT model, leveraging both unsupervised and supervised learning
techniques to identify anomalous activity while minimizing data labeling. The
unsupervised method captures the underlying structure and syntax of Unix shell
commands, enabling the detection of session deviations from normal behavior.
Experiments on a large-scale enterprise dataset collected from production
systems demonstrate the effectiveness of our approach in detecting anomalous
behavior in Unix shell sessions. This work highlights the potential of
leveraging recent advances in transformers to address important computer
security challenges.

通过使用预训练的 DistilBERT 模型，结合无监督学习和监督学习技术，实现了在 Unix shell 会话中检测异常行为的全面方法，从而捕捉到与正常行为不符的会话偏差，证实了利用最新的 transformer 技术解决重要的计算机安全挑战的潜力。

基于 DistilBERT 的命令行会话异常检测：无监督和有监督方法

Anomaly Detection of Command Shell Sessions based on DistilBERT:  Unsupervised and Supervised Approaches

Entity-level sentiment analysis predicts the sentiment about entities
mentioned in a given text. It is very useful in a business context to
understand user emotions towards certain entities, such as products or
companies. In this paper, we demonstrate how we developed an entity-level
sentiment analysis system that analyzes English telephone conversation
transcripts in contact centers to provide business insight. We present two
approaches, one entirely based on the transformer-based DistilBERT model, and
another that uses a convolutional neural network supplemented with some
heuristic rules.

本文提出了一种基于 DistilBERT 模型完全的方法和一种卷积神经网络的方法，通过分析英语电话交谈转录本来进行实体级情感分析，以期提供商业见解。