Biometrics is used to authenticate an individual based on physiological or
behavioral traits. Mouse dynamics is an example of a behavioral biometric that
can be used to perform continuous authentication as protection against security
breaches. Recent research on mouse dynamics has shown promising results in
identifying users; however, it has not yet reached an acceptable level of
accuracy. In this paper, an empirical evaluation of different classification
techniques is conducted on a mouse dynamics dataset, the Balabit Mouse
Challenge dataset. User identification is carried out using three mouse
actions: mouse move, point and click, and drag and drop. Verification and
authentication methods are conducted using three machine-learning classifiers:
the Decision Tree classifier, the K-Nearest Neighbors classifier, and the
Random Forest classifier. The results show that the three classifiers can
distinguish between a genuine user and an impostor with a relatively high
degree of accuracy. In the verification mode, all the classifiers achieve a
perfect accuracy of 100%. In authentication mode, all three classifiers
achieved the highest accuracy (ACC) and Area Under Curve (AUC) from scenario B
using the point and click action data: (Decision Tree ACC:87.6%, AUC:90.3%),
(K-Nearest Neighbors ACC:99.3%, AUC:99.9%), and (Random Forest ACC:89.9%,
AUC:92.5%).

该研究使用生物特征识别对个人进行身份认证，其中鼠标动力学是一种行为生物特征，可以用于进行连续身份验证以防止安全漏洞。该论文通过对鼠标动力学数据集 Balabit Mouse Challenge 数据集进行了不同分类技术的实证评估，使用鼠标移动、点选和拖放三种动作进行用户识别，并使用决策树分类器、K 最近邻分类器和随机森林分类器进行验证和认证。结果显示，这三种分类器可以以相对较高的准确率区分真实用户和冒名顶替者。在验证模式下，所有分类器的准确率达到 100%。在认证模式下，三种分类器在使用点选操作数据的情景 B 中实现了最高准确率（决策树准确率：87.6%，AUC：90.3%），（K 最近邻准确率：99.3%，AUC：99.9%）和（随机森林准确率：89.9%，AUC：92.5%）。

使用鼠标点击数据分析的连续认证

Continuous Authentication Using Mouse Clickstream Data Analysis

There have been many papers with algorithms for improving fairness of
machine-learning classifiers for tabular data. Unfortunately, most use only
very few datasets for their experimental evaluation. We introduce a suite of
functions for fetching 20 fairness datasets and providing associated fairness
metadata. Hopefully, these will lead to more rigorous experimental evaluations
in future fairness-aware machine learning research.

通过引入一组函数以获取 20 个公平数据集并提供相关的公平元数据，希望能在未来的公平感知机器学习研究中进行更严格的实验评估。

一套用于表格分类的公平数据集

A Suite of Fairness Datasets for Tabular Classification

$\textit{Fake followers}$ are those Twitter accounts specifically created to
inflate the number of followers of a target account. Fake followers are
dangerous for the social platform and beyond, since they may alter concepts
like popularity and influence in the Twittersphere - hence impacting on
economy, politics, and society. In this paper, we contribute along different
dimensions. First, we review some of the most relevant existing features and
rules (proposed by Academia and Media) for anomalous Twitter accounts
detection. Second, we create a baseline dataset of verified human and fake
follower accounts. Such baseline dataset is publicly available to the
scientific community. Then, we exploit the baseline dataset to train a set of
machine-learning classifiers built over the reviewed rules and features. Our
results show that most of the rules proposed by Media provide unsatisfactory
performance in revealing fake followers, while features proposed in the past by
Academia for spam detection provide good results. Building on the most
promising features, we revise the classifiers both in terms of reduction of
overfitting and cost for gathering the data needed to compute the features. The
final result is a novel $\textit{Class A}$ classifier, general enough to thwart
overfitting, lightweight thanks to the usage of the less costly features, and
still able to correctly classify more than 95% of the accounts of the original
training set. We ultimately perform an information fusion-based sensitivity
analysis, to assess the global sensitivity of each of the features employed by
the classifier. The findings reported in this paper, other than being supported
by a thorough experimental methodology and interesting on their own, also pave
the way for further investigation on the novel issue of fake Twitter followers.

通过构建 Twitter 真实与虚假跟随者的基准数据集，本文训练了一组基于机器学习的分类器，证明了部分学术界提出的用于垃圾邮件检测的特征表现良好，建立了一个新的 “Class A” 分类器，能够正确分类超过 95% 的账户，并进行了灵敏度分析。