The $5^{th}$ generation of mobile networks introduces a new Network Function
(NF) that was not present in previous generations, namely the Network Data
Analytics Function (NWDAF). Its primary objective is to provide advanced
analytics services to various entities within the network and also towards
external application services in the 5G ecosystem. One of the key use cases of
NWDAF is mobility trajectory prediction, which aims to accurately support
efficient mobility management of User Equipment (UE) in the network by
allocating ``just in time'' necessary network resources. In this paper, we show
that there are potential mobility attacks that can compromise the accuracy of
these predictions. In a semi-realistic scenario with 10,000 subscribers, we
demonstrate that an adversary equipped with the ability to hijack cellular
mobile devices and clone them can significantly reduce the prediction accuracy
from 75\% to 40\% using just 100 adversarial UEs. While a defense mechanism
largely depends on the attack and the mobility types in a particular area, we
prove that a basic KMeans clustering is effective in distinguishing legitimate
and adversarial UEs.

第五代移动通信网络引入了新的网络功能（NF），即网络数据分析功能（NWDAF）。NWDAF 的主要目标是向网络内的各实体以及 5G 生态系统中的外部应用服务提供先进的分析服务。NWDAF 的一个关键用例是移动轨迹预测，旨在通过及时分配必要的网络资源来准确支持网络中用户设备（UE）的高效移动管理。本文展示了可能危害这些预测准确性的潜在移动攻击。在一个半真实场景中，我们演示了一个拥有窃取蜂窝移动设备和克隆设备能力的对手可以仅使用 100 个对抗性 UE 将预测准确性从 75% 降低至 40%。虽然防御机制在很大程度上取决于攻击和特定区域的移动类型，我们证明基本的 K 均值聚类方法有效地区分合法用户设备和对抗性用户设备。

5G 网络中的移动预测攻击

Attacks Against Mobility Prediction in 5G Networks

In the last two decades, automatic extractive text summarization on lectures
has demonstrated to be a useful tool for collecting key phrases and sentences
that best represent the content. However, many current approaches utilize dated
approaches, producing sub-par outputs or requiring several hours of manual
tuning to produce meaningful results. Recently, new machine learning
architectures have provided mechanisms for extractive summarization through the
clustering of output embeddings from deep learning models. This paper reports
on the project called Lecture Summarization Service, a python based RESTful
service that utilizes the BERT model for text embeddings and KMeans clustering
to identify sentences closes to the centroid for summary selection. The purpose
of the service was to provide students a utility that could summarize lecture
content, based on their desired number of sentences. On top of the summary
work, the service also includes lecture and summary management, storing content
on the cloud which can be used for collaboration. While the results of
utilizing BERT for extractive summarization were promising, there were still
areas where the model struggled, providing feature research opportunities for
further improvement.

本篇论文介绍了使用 Python 的 RESTful 服务 Lecture Summarization Service，利用 BERT 模型和 KMeans 聚类算法对课程内容进行自动摘要，以提供学生便捷的学习方式。该方法虽然有待进一步改进，但是已经取得了令人满意的效果。