Intrusion detection systems (IDSs) are essential elements of IT systems.
Their key component is a classification module that continuously evaluates some
features of the network traffic and identifies possible threats. Its efficiency
is greatly affected by the right selection of the features to be monitored.
Therefore, the identification of a minimal set of features that are necessary
to safely distinguish malicious traffic from benign traffic is indispensable in
the course of the development of an IDS. This paper presents the preprocessing
and feature selection workflow as well as its results in the case of the
CSE-CIC-IDS2018 on AWS dataset, focusing on five attack types. To identify the
relevant features, six feature selection methods were applied, and the final
ranking of the features was elaborated based on their average score. Next,
several subsets of the features were formed based on different ranking
threshold values, and each subset was tried with five classification algorithms
to determine the optimal feature set for each attack type. During the
evaluation, four widely used metrics were taken into consideration.

本文基于 CSE-CIC-IDS2018 on AWS 数据集，针对五种攻击类型，通过四种广泛使用的指标，应用六种特征选择方法，形成了多个特征子集，使用五个分类算法确定每种攻击类型的最佳特征集。

为 CSE-CIC-IDS2018 数据集开发入侵检测系统识别相关特征

Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the  Development of an Intrusion Detection System

Action detection is an essential and challenging task, especially for densely
labelled datasets of untrimmed videos. The temporal relation is complex in
those datasets, including challenges like composite action, and co-occurring
action. For detecting actions in those complex videos, efficiently capturing
both short-term and long-term temporal information in the video is critical. To
this end, we propose a novel ConvTransformer network for action detection. This
network comprises three main components: (1) Temporal Encoder module
extensively explores global and local temporal relations at multiple temporal
resolutions. (2) Temporal Scale Mixer module effectively fuses the multi-scale
features to have a unified feature representation. (3) Classification module is
used to learn the instance center-relative position and predict the frame-level
classification scores. The extensive experiments on multiple datasets,
including Charades, TSU and MultiTHUMOS, confirm the effectiveness of our
proposed method. Our network outperforms the state-of-the-art methods on all
three datasets.

我们提出了一种基于 ConvTransformer 网络的行动检测方法，通过三个组件（时间编码器、时间尺度混合器和分类器）高效地捕捉视频中的短期和长期时间信息，实验表明该方法在 Charades、TSU 和 MultiTHUMOS 数据集上效果显著优于现有方法。

MS-TCT: 多尺度时间卷积转换器用于动作检测

MS-TCT: Multi-Scale Temporal ConvTransformer for Action Detection

Temporal action localization is an important step towards video
understanding. Most current action localization methods depend on untrimmed
videos with full temporal annotations of action instances. However, it is
expensive and time-consuming to annotate both action labels and temporal
boundaries of videos. To this end, we propose a weakly supervised temporal
action localization method that only requires video-level action instances as
supervision during training. We propose a classification module to generate
action labels for each segment in the video, and a deep metric learning module
to learn the similarity between different action instances. We jointly optimize
a balanced binary cross-entropy loss and a metric loss using a standard
backpropagation algorithm. Extensive experiments demonstrate the effectiveness
of both of these components in temporal localization. We evaluate our algorithm
on two challenging untrimmed video datasets: THUMOS14 and ActivityNet1.2. Our
approach improves the current state-of-the-art result for THUMOS14 by 6.5% mAP
at IoU threshold 0.5, and achieves competitive performance for ActivityNet1.2.

本文介绍了一种弱监督的时间动作定位方法，该方法只需要视频级别的动作实例作为训练数据，在视频的每个片段中生成行为标签，并学习不同动作实例之间的相似性，实验表明该方法在两个视频数据集上取得了有竞争力的结果。