We study the problem of high-dimensional robust mean estimation in an online
setting. Specifically, we consider a scenario where $n$ sensors are measuring
some common, ongoing phenomenon. At each time step $t=1,2,\ldots,T$, the
$i^{th}$ sensor reports its readings $x^{(i)}_t$ for that time step. The
algorithm must then commit to its estimate $\mu_t$ for the true mean value of
the process at time $t$. We assume that most of the sensors observe independent
samples from some common distribution $X$, but an $\epsilon$-fraction of them
may instead behave maliciously. The algorithm wishes to compute a good
approximation $\mu$ to the true mean $\mu^\ast := \mathbf{E}[X]$. We note that
if the algorithm is allowed to wait until time $T$ to report its estimate, this
reduces to the well-studied problem of robust mean estimation. However, the
requirement that our algorithm produces partial estimates as the data is coming
in substantially complicates the situation.
We prove two main results about online robust mean estimation in this model.
First, if the uncorrupted samples satisfy the standard condition of
$(\epsilon,\delta)$-stability, we give an efficient online algorithm that
outputs estimates $\mu_t$, $t \in [T],$ such that with high probability it
holds that $\|\mu-\mu^\ast\|_2 = O(\delta \log(T))$, where $\mu = (\mu_t)_{t
\in [T]}$. We note that this error bound is nearly competitive with the best
offline algorithms, which would achieve $\ell_2$-error of $O(\delta)$. Our
second main result shows that with additional assumptions on the input (most
notably that $X$ is a product distribution) there are inefficient algorithms
whose error does not depend on $T$ at all.

在线高维健壮均值估计问题的研究，讨论传感器在在线环境下的独立采样和恶意行为，给出了一个高效算法以及附加假设下的低效算法。

在线稳健均值估计

Online Robust Mean Estimation

There is a limited amount of publicly available data to support research in
malware analysis technology. Particularly, there are virtually no publicly
available datasets generated from rich sandboxes such as Cuckoo/CAPE. The
benefit of using dynamic sandboxes is the realistic simulation of file
execution in the target machine and obtaining a log of such execution. The
machine can be infected by malware hence there is a good chance of capturing
the malicious behavior in the execution logs, thus allowing researchers to
study such behavior in detail. Although the subsequent analysis of log
information is extensively covered in industrial cybersecurity backends, to our
knowledge there has been only limited effort invested in academia to advance
such log analysis capabilities using cutting edge techniques. We make this
sample dataset available to support designing new machine learning methods for
malware detection, especially for automatic detection of generic malicious
behavior. The dataset has been collected in cooperation between Avast Software
and Czech Technical University - AI Center (AIC).

提供一个样本数据集来支持设计新的机器学习方法用于恶意软件检测，特别是用于自动检测通用恶意行为，该数据集由 Avast 软件和捷克理工大学 - AI 中心（AIC）合作收集。

Avast-CTU 公共 CAPE 数据集

Avast-CTU Public CAPE Dataset

Intuitively, a backdoor attack against Deep Neural Networks (DNNs) is to
inject hidden malicious behaviors into DNNs such that the backdoor model
behaves legitimately for benign inputs, yet invokes a predefined malicious
behavior when its input contains a malicious trigger. The trigger can take a
plethora of forms, including a special object present in the image (e.g., a
yellow pad), a shape filled with custom textures (e.g., logos with particular
colors) or even image-wide stylizations with special filters (e.g., images
altered by Nashville or Gotham filters). These filters can be applied to the
original image by replacing or perturbing a set of image pixels.

研究表明：恶意后门注入是指将恶意行为隐藏在深度神经网络中，在输入数据不包含恶意触发器时行为正常，但含有特定触发器时会调用事先定义好的恶意行为，触发器可以采用不同形式，这可以通过替换或扰动一组图像像素来对原始图像应用滤镜进行操作。