There has been a large number of studies in interpretable and explainable ML
for cybersecurity, in particular, for intrusion detection. Many of these
studies have significant amount of overlapping and repeated evaluations and
analysis. At the same time, these studies overlook crucial model, data,
learning process, and utility related issues and many times completely
disregard them. These issues include the use of overly complex and opaque ML
models, unaccounted data imbalances and correlated features, inconsistent
influential features across different explanation methods, the inconsistencies
stemming from the constituents of a learning process, and the implausible
utility of explanations. In this work, we empirically demonstrate these issues,
analyze them and propose practical solutions in the context of feature-based
model explanations. Specifically, we advise avoiding complex opaque models such
as Deep Neural Networks and instead using interpretable ML models such as
Decision Trees as the available intrusion datasets are not difficult for such
interpretable models to classify successfully. Then, we bring attention to the
binary classification metrics such as Matthews Correlation Coefficient (which
are well-suited for imbalanced datasets. Moreover, we find that feature-based
model explanations are most often inconsistent across different settings. In
this respect, to further gauge the extent of inconsistencies, we introduce the
notion of cross explanations which corroborates that the features that are
determined to be impactful by one explanation method most often differ from
those by another method. Furthermore, we show that strongly correlated data
features and the constituents of a learning process, such as hyper-parameters
and the optimization routine, become yet another source of inconsistent
explanations. Finally, we discuss the utility of feature-based explanations.

该研究揭示了解释性机器学习在网络安全中的重要性，发现模型和数据的相关问题，提出了使用可解释的机器学习模型以及解决数据不平衡和解释一致性的实际方法。

可解释和可解释的机器学习在入侵检测中的重要评估

A Critical Assessment of Interpretable and Explainable Machine Learning  for Intrusion Detection

The dynamic and unpredictable nature of road traffic necessitates effective
accident detection methods for enhancing safety and streamlining traffic
management in smart cities. This paper offers a comprehensive exploration study
of prevailing accident detection techniques, shedding light on the nuances of
other state-of-the-art methodologies while providing a detailed overview of
distinct traffic accident types like rear-end collisions, T-bone collisions,
and frontal impact accidents. Our novel approach introduces the I3D-CONVLSTM2D
model architecture, a lightweight solution tailored explicitly for accident
detection in smart city traffic surveillance systems by integrating RGB frames
with optical flow information. Our experimental study's empirical analysis
underscores our approach's efficacy, with the I3D-CONVLSTM2D RGB + Optical-Flow
(Trainable) model outperforming its counterparts, achieving an impressive 87\%
Mean Average Precision (MAP). Our findings further elaborate on the challenges
posed by data imbalances, particularly when working with a limited number of
datasets, road structures, and traffic scenarios. Ultimately, our research
illuminates the path towards a sophisticated vision-based accident detection
system primed for real-time integration into edge IoT devices within smart
urban infrastructures.

本研究对现有的事故检测技术进行了全面的探索研究，重点介绍了尾部碰撞、侧面碰撞和正面碰撞等不同类型的交通事故，并引入了适用于智慧城市交通监控系统的 I3D-CONVLSTM2D 模型架构，结合 RGB 帧和光流信息进行事故检测。实验分析验证了该方法的有效性，该模型在平均精度（Mean Average Precision，MAP）方面表现优异，达到了 87%。同时，本研究还详细阐述了数据不平衡在有限数据集、道路结构和交通场景方面带来的挑战，最终为基于视觉的事故检测系统在智慧城市基础设施中实时集成到边缘物联网设备提供了路径指引。

智慧城市交通：用深度学习集成方法进行交通事故检测

Smart City Transportation: Deep Learning Ensemble Approach for Traffic  Accident Detection

Various data imbalances that naturally arise in a multi-territory
personalized recommender system can lead to a significant item bias for
globally prevalent items. A locally popular item can be overshadowed by a
globally prevalent item. Moreover, users' viewership patterns/statistics can
drastically change from one geographic location to another which may suggest to
learn specific user embeddings. In this paper, we propose a multi-task learning
(MTL) technique, along with an adaptive upsampling method to reduce popularity
bias in multi-territory recommendations. Our proposed framework is designed to
enrich training examples with active users representation through upsampling,
and capable of learning geographic-based user embeddings by leveraging MTL.
Through experiments, we demonstrate the effectiveness of our framework in
multiple territories compared to a baseline not incorporating our proposed
techniques.~Noticeably, we show improved relative gain of up to $65.27\%$ in
PR-AUC metric. A case study is presented to demonstrate the advantages of our
methods in attenuating the popularity bias of global items.

通过提出多任务学习技术和自适应上采样方法来减少多领域推荐中的受欢迎度偏差，提出的方法通过上采样来丰富训练样例，并通过利用多任务学习来学习基于地理位置的用户嵌入，实验证明了方法在多个地域上相对于不采用提出的技术的基准模型的有效性，显著改进了 PR-AUC 指标高达 65.27%，并通过案例研究展示了方法在减轻全局物品的受欢迎度偏差方面的优势。