Spectrum sharing allows different protocols of the same standard (e.g.,
802.11 family) or different standards (e.g., LTE and DVB) to coexist in
overlapping frequency bands. As this paradigm continues to spread, wireless
systems must also evolve to identify active transmitters and unauthorized
waveforms in real time under intentional distortion of preambles, extremely low
signal-to-noise ratios and challenging channel conditions. We overcome
limitations of correlation-based preamble matching methods in such conditions
through the design of T-PRIME: a Transformer-based machine learning approach.
T-PRIME learns the structural design of transmitted frames through its
attention mechanism, looking at sequence patterns that go beyond the preamble
alone. The paper makes three contributions: First, it compares Transformer
models and demonstrates their superiority over traditional methods and
state-of-the-art neural networks. Second, it rigorously analyzes T-PRIME's
real-time feasibility on DeepWave's AIR-T platform. Third, it utilizes an
extensive 66 GB dataset of over-the-air (OTA) WiFi transmissions for training,
which is released along with the code for community use. Results reveal nearly
perfect (i.e. $>98\%$) classification accuracy under simulated scenarios,
showing $100\%$ detection improvement over legacy methods in low SNR ranges,
$97\%$ classification accuracy for OTA single-protocol transmissions and up to
$75\%$ double-protocol classification accuracy in interference scenarios.

通过 T-PRIME：一种基于 Transformer 的机器学习方法，本文在具有故意失真、极低信噪比和具有挑战性信道条件下，通过设计 T-PRIME 的注意机制来学习发送帧的结构设计，从而克服了相关性匹配方法的局限性。文章通过比较 Transformer 模型与传统方法和最先进的神经网络的优越性、在 DeepWave 的 AIR-T 平台上对 T-PRIME 的实时可行性进行严格分析，以及利用开放 66 GB 的实时 OTA（Over-The-Air）WiFi 传输数据集进行训练，并发布了代码供社区使用，得出结果显示在模拟场景中几乎完美（即 > 98%）的分类准确率，显示在低信噪比范围内相比于传统方法有 100% 的检测改进，OTA 单协议传输的分类准确率为 97%，干扰场景下的双协议分类准确率高达 75%。

基于 Transformer 的机器学习边缘协议识别

T-PRIME: Transformer-based Protocol Identification for Machine-learning  at the Edge

Transformer-based machine learning models have become an essential tool for
many natural language processing (NLP) tasks since the introduction of the
method. A common objective of these projects is to classify text data.
Classification models are often extended to a different topic and/or time
period. In these situations, deciding how long a classification is suitable for
and when it is worth re-training our model is difficult. This paper compares
different approaches to fine-tune a BERT model for a long-running
classification task. We use data from different periods to fine-tune our
original BERT model, and we also measure how a second round of annotation could
boost the classification quality. Our corpus contains over 8 million comments
on COVID-19 vaccination in Hungary posted between September 2020 and December
2021. Our results show that the best solution is using all available unlabeled
comments to fine-tune a model. It is not advisable to focus only on comments
containing words that our model has not encountered before; a more efficient
solution is randomly sample comments from the new period. Fine-tuning does not
prevent the model from losing performance but merely slows it down. In a
rapidly changing linguistic environment, it is not possible to maintain model
performance without regularly annotating new text.

本研究比较不同方法用于长期分类任务的 BERT 模型的微调，发现使用所有可用的未标记评论来微调模型是最佳解决方案，而仅关注模型之前未遇到的单词的评论并不可取，其中一个更有效的解决方案是从新时期的评论中随机抽样评论。

提高自然语言处理变形金刚模型长期的分类可靠性

Boosting classification reliability of NLP transformer models in the long run

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.

本文探讨了机器学习在分析邮件中的内容、检测潜在恶意和创建邮件摘要方面的应用，以帮助用户判断是否安全及自学习更高级的恶意模式。