Spear-phishing attacks present a significant security challenge, with large
language models (LLMs) escalating the threat by generating convincing emails
and facilitating target reconnaissance. To address this, we propose a detection
approach based on a novel document vectorization method that utilizes an
ensemble of LLMs to create representation vectors. By prompting LLMs to reason
and respond to human-crafted questions, we quantify the presence of common
persuasion principles in the email's content, producing prompted contextual
document vectors for a downstream supervised machine learning model. We
evaluate our method using a unique dataset generated by a proprietary system
that automates target reconnaissance and spear-phishing email creation. Our
method achieves a 91% F1 score in identifying LLM-generated spear-phishing
emails, with the training set comprising only traditional phishing and benign
emails. Key contributions include an innovative document vectorization method
utilizing LLM reasoning, a publicly available dataset of high-quality
spear-phishing emails, and the demonstrated effectiveness of our method in
detecting such emails. This methodology can be utilized for various document
classification tasks, particularly in adversarial problem domains.

基于新的文档向量化方法，我们提出了一种检测方法，利用大型语言模型的共同作用来生成表示向量，以解决鱼叉式网络钓鱼攻击问题，并证明了该方法的有效性，可以应用于各种文档分类任务，特别是在对抗性问题领域。

启发式上下文向量用于鱼叉式网络钓鱼检测

Prompted Contextual Vectors for Spear-Phishing Detection

Line outage identification in distribution grids is essential for sustainable
grid operation. In this work, we propose a practical yet robust detection
approach that utilizes only readily available voltage magnitudes, eliminating
the need for costly phase angles or power flow data. Given the sensor data,
many existing detection methods based on change-point detection require prior
knowledge of outage patterns, which are unknown for real-world outage
scenarios. To remove this impractical requirement, we propose a data-driven
method to learn the parameters of the post-outage distribution through gradient
descent. However, directly using gradient descent presents feasibility issues.
To address this, we modify our approach by adding a Bregman divergence
constraint to control the trajectory of the parameter updates, which eliminates
the feasibility problems. As timely operation is the key nowadays, we prove
that the optimal parameters can be learned with convergence guarantees via
leveraging the statistical and physical properties of voltage data. We evaluate
our approach using many representative distribution grids and real load
profiles with 17 outage configurations. The results show that we can detect and
localize the outage in a timely manner with only voltage magnitudes and without
assuming a prior knowledge of outage patterns.

在这项研究中，我们提出了一种实用而稳健的检测方法，仅利用现有的电压幅值，消除了昂贵的相角或功率流数据的需求，并通过梯度下降学习后事故分布的参数，以及添加 Bregman 散度约束解决可行性问题，证明可以及时检测和定位停电，无需假设已知停电模式。

具有未知模式和性能保障的配电网线路故障识别

Distribution Grid Line Outage Identification with Unknown Pattern and  Performance Guarantee

The detection of anatomical landmarks is a vital step for medical image
analysis and applications for diagnosis, interpretation and guidance. Manual
annotation of landmarks is a tedious process that requires domain-specific
expertise and introduces inter-observer variability. This paper proposes a new
detection approach for multiple landmarks based on multi-agent reinforcement
learning. Our hypothesis is that the position of all anatomical landmarks is
interdependent and non-random within the human anatomy, thus finding one
landmark can help to deduce the location of others. Using a Deep Q-Network
(DQN) architecture we construct an environment and agent with implicit
inter-communication such that we can accommodate K agents acting and learning
simultaneously, while they attempt to detect K different landmarks. During
training the agents collaborate by sharing their accumulated knowledge for a
collective gain. We compare our approach with state-of-the-art architectures
and achieve significantly better accuracy by reducing the detection error by
50%, while requiring fewer computational resources and time to train compared
to the naive approach of training K agents separately.

本文提出了一种基于多智能体强化学习的多个解剖标志点检测方法，使用 Deep Q-Network（DQN）架构构建环境和代理，并在训练过程中协作共享累积的知识，相较于现有技术方案，该方法将检测误差减少了 50％, 需要较少的计算资源和训练时间。