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

Federated Graph Neural Network (FedGNN) has recently emerged as a rapidly
growing research topic, as it integrates the strengths of graph neural networks
and federated learning to enable advanced machine learning applications without
direct access to sensitive data. Despite its advantages, the distributed nature
of FedGNN introduces additional vulnerabilities, particularly backdoor attacks
stemming from malicious participants. Although graph backdoor attacks have been
explored, the compounded complexity introduced by the combination of GNNs and
federated learning has hindered a comprehensive understanding of these attacks,
as existing research lacks extensive benchmark coverage and in-depth analysis
of critical factors. To address these limitations, we propose Bkd-FedGNN, a
benchmark for backdoor attacks on FedGNN. Specifically, Bkd-FedGNN decomposes
the graph backdoor attack into trigger generation and injection steps, and
extending the attack to the node-level federated setting, resulting in a
unified framework that covers both node-level and graph-level classification
tasks. Moreover, we thoroughly investigate the impact of multiple critical
factors in backdoor attacks on FedGNN. These factors are categorized into
global-level and local-level factors, including data distribution, the number
of malicious attackers, attack time, overlapping rate, trigger size, trigger
type, trigger position, and poisoning rate. Finally, we conduct comprehensive
evaluations on 13 benchmark datasets and 13 critical factors, comprising 1,725
experimental configurations for node-level and graph-level tasks from six
domains. These experiments encompass over 8,000 individual tests, allowing us
to provide a thorough evaluation and insightful observations that advance our
understanding of backdoor attacks on FedGNN.The Bkd-FedGNN benchmark is
publicly available at this https URL

Bkd-FedGNN 是 FedGNN 后门攻击的基准测试，将图后门攻击分解为触发器生成和注入步骤，并将攻击扩展到节点级联合设置，提供了一个统一的框架，涵盖了节点级和图级分类任务。

Bkd-FedGNN: 联邦图神经网络分类后门攻击基准

Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated  Graph Neural Network

Recently, advanced NLP models have seen a surge in the usage of various
applications. This raises the security threats of the released models. In
addition to the clean models' unintentional weaknesses, {\em i.e.,} adversarial
attacks, the poisoned models with malicious intentions are much more dangerous
in real life. However, most existing works currently focus on the adversarial
attacks on NLP models instead of positioning attacks, also named
\textit{backdoor attacks}. In this paper, we first propose the \textit{natural
backdoor attacks} on NLP models. Moreover, we exploit the various attack
strategies to generate trigger on text data and investigate different types of
triggers based on modification scope, human recognition, and special cases.
Last, we evaluate the backdoor attacks, and the results show the excellent
performance of with 100\% backdoor attacks success rate and sacrificing of
0.83\% on the text classification task.

本研究提出了针对 NLP 模型的 “自然后门攻击”，对文本数据进行触发器生成，并研究了不同类型的触发器，最终在文本分类任务上实现了 100% 的后门攻击成功率和 0.83% 的牺牲。