Deep anomaly detection on sequential data has garnered significant attention
due to the wide application scenarios. However, deep learning-based models face
a critical security threat - their vulnerability to backdoor attacks. In this
paper, we explore compromising deep sequential anomaly detection models by
proposing a novel backdoor attack strategy. The attack approach comprises two
primary steps, trigger generation and backdoor injection. Trigger generation is
to derive imperceptible triggers by crafting perturbed samples from the benign
normal data, of which the perturbed samples are still normal. The backdoor
injection is to properly inject the backdoor triggers to comprise the model
only for the samples with triggers. The experimental results demonstrate the
effectiveness of our proposed attack strategy by injecting backdoors on two
well-established one-class anomaly detection models.

探索深度序列异常检测模型的后门攻击策略，通过生成触发器和注入后门触发器，有效地破坏现有异常检测模型。

对一类序列异常检测模型的后门攻击

Backdoor Attack against One-Class Sequential Anomaly Detection Models

Most deep anomaly detection models are based on learning normality from
datasets due to the difficulty of defining abnormality by its diverse and
inconsistent nature. Therefore, it has been a common practice to learn
normality under the assumption that anomalous data are absent in a training
dataset, which we call normality assumption. However, in practice, the
normality assumption is often violated due to the nature of real data
distributions that includes anomalous tails, i.e., a contaminated dataset.
Thereby, the gap between the assumption and actual training data affects
detrimentally in learning of an anomaly detection model. In this work, we
propose a learning framework to reduce this gap and achieve better normality
representation. Our key idea is to identify sample-wise normality and utilize
it as an importance weight, which is updated iteratively during the training.
Our framework is designed to be model-agnostic and hyperparameter insensitive
so that it applies to a wide range of existing methods without careful
parameter tuning. We apply our framework to three different representative
approaches of deep anomaly detection that are classified into one-class
classification-, probabilistic model-, and reconstruction-based approaches. In
addition, we address the importance of a termination condition for iterative
methods and propose a termination criterion inspired by the anomaly detection
objective. We validate that our framework improves the robustness of the
anomaly detection models under different levels of contamination ratios on five
anomaly detection benchmark datasets and two image datasets. On various
contaminated datasets, our framework improves the performance of three
representative anomaly detection methods, measured by area under the ROC curve.

减小理论假设与实际训练数据之间的差距，并提出一个学习框架来改进正常性表示，通过识别样本的正常性，并在训练过程中迭代更新样本的重要性权重。还提出了终止条件，受到异常检测目标的启发。通过在多个受污染数据集上进行实验证明，该框架提高了三种典型异常检测方法的鲁棒性。

数据污染下无监督鲁棒异常检测的迭代方法

An Iterative Method for Unsupervised Robust Anomaly Detection Under Data  Contamination

Online anomaly detection from a data stream is critical for the safety and
security of many applications but is facing severe challenges due to complex
and evolving data streams from IoT devices and cloud-based infrastructures.
Unfortunately, existing approaches fall too short for these challenges; online
anomaly detection methods bear the burden of handling the complexity while
offline deep anomaly detection methods suffer from the evolving data
distribution. This paper presents a framework for online deep anomaly
detection, ARCUS, which can be instantiated with any autoencoder-based deep
anomaly detection methods. It handles the complex and evolving data streams
using an adaptive model pooling approach with two novel techniques:
concept-driven inference and drift-aware model pool update; the former detects
anomalies with a combination of models most appropriate for the complexity, and
the latter adapts the model pool dynamically to fit the evolving data streams.
In comprehensive experiments with ten data sets which are both high-dimensional
and concept-drifted, ARCUS improved the anomaly detection accuracy of the
streaming variants of state-of-the-art autoencoder-based methods and that of
the state-of-the-art streaming anomaly detection methods by up to 22% and 37%,
respectively.

该研究提出了一种名为 ARCUS 的在线深度异常检测框架，它使用自适应模型池方法和两个新技术：概念驱动推理和漂移感知模型池更新来处理来自物联网设备和云基础架构的复杂和不断演化的数据流，并在实验中证明其在高维和概念漂移数据集上提高了最先进的自编码器方法和流式异常检测方法的检测准确性。