与网络无关的状态机复制

A Formally Verified Protocol for Log Replication with Byzantine Fault Tolerance

Byzantine fault tolerant protocols enable state replication in the presence of crashed, malfunctioning, or actively malicious processes. Designing such protocols without the assistance of verification tools, however, is remarkably error-prone. In an adversarial environment, performance and flexibility come at the cost of complexity, making the verification of existing protocols extremely difficult. We take a different approach and propose a formally verified consensus protocol designed for a specific use case: secure logging. Our protocol allows each node to propose entries in a parallel subro…

拜占庭广域复制的鲁棒自动化重配置

Robust and Automated Reconfiguration of Byzantine Wide-Area Replication

针对拜占庭容错复制中的配置优化漏洞，提出Beware框架，通过过滤伪造延迟报告、计算鲁棒权重分布和机器学习实现拜占庭弹性配置，延迟降低45%。

拜占庭容错复制中的非确定性

Non-determinism in Byzantine Fault-Tolerant Replication

服务复制将应用程序分布在多个进程上，以容忍进程子集中的故障、攻击和不当行为。已建立的状态机复制范例本质上要求应用程序具有确定性。本文区分了三种模型来处理复制服务中的非确定性，其中一些进程会受到故障和任意行为的影响（所谓的拜占庭故障）：首先，一种模块化方法，不需要对潜在的非确定性应用程序进行任何更改（也不能访问其内部数据）;第二，主从方法，其中领导者打破联系，其他进程验证领导者的选择;最后，处理使用密码学和密钥的应用程序。密码操作和秘密必须被特殊对待，因为它们需要很强的随机性来满足它们的目标。本文还介绍了两个新的协议。第一种方法使用模块化方法过滤掉应用程序中的非确定性操作。它确保所有正确的过程产生相同的输出，并且它们的内部状态不会发散。第二个协议实现了密码安全的随机性生成与可验证的随机函数，并适用于某些安全模型。所有协议都以通用的方式描述，并且不假设底层共识原语的特定实现。

N版本混淆：利用程序多样性阻止软件篡改复制

N-Version Obfuscation: Impeding Software Tampering Replication with Program Diversity

防篡改是软件安全的一个重要研究领域。已经提出了许多方法来阻止特定的篡改过程，例如，混淆和自校验和。然而，据我们所知，它们都不能达到理论上的防篡改。我们的想法是通过程序的多样化来阻止篡改的复制，从而增加破解整个软件系统的复杂性。为此，我们建议提供相同的功能，但功能不等同的软件副本到不同的机器。我们正式定义的问题N版本混淆，并提供了一个可行的手段来解决这个问题。我们的评估结果表明，打破软件系统所需的时间是线性增加的软件版本的数量，这是O（n）的复杂度。

Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning

Explainable machine learning has become increasingly prevalent, especially in healthcare where explainable models are vital for ethical and trusted automated decision making. Work on the susceptibility of deep learning models to adversarial attacks has shown the ease of designing samples to mislead a model into making incorrect predictions. In this work, we propose a model agnostic explainability-based method for the accurate detection of adversarial samples on two datasets with different complexity and properties: Electronic Health Record (EHR) and chest X-ray (CXR) data. On the MIMIC-III and…

Model Agnostic Defence against Backdoor Attacks in Machine Learning

Machine Learning (ML) has automated a multitude of our day-to-day decision making domains such as education, employment and driving automation. The continued success of ML largely depends on our ability to trust the model we are using. Recently, a new class of attacks called Backdoor Attacks have been developed. These attacks undermine the user's trust in ML models. In this work, we present NEO, a model agnostic framework to detect and mitigate such backdoor attacks in image classification ML models. For a given image classification model, our approach analyses the inputs it receives and deter…

Magmaw：针对基于机器学习的无线通信系统的模态无关对抗攻击

Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems

本文提出Magmaw，一种模态无关的无线对抗攻击方法，可为任意多模态无线信号生成通用扰动，并在实时软件定义无线电平台上验证其能突破多种防御、显著降低基于机器学习的无线系统性能。

AdvCat: Domain-Agnostic Robustness Assessment for Cybersecurity-Critical Applications with Categorical Inputs

Machine Learning-as-a-Service systems (MLaaS) have been largely developed for cybersecurity-critical applications, such as detecting network intrusions and fake news campaigns. Despite effectiveness, their robustness against adversarial attacks is one of the key trust concerns for MLaaS deployment. We are thus motivated to assess the adversarial robustness of the Machine Learning models residing at the core of these security-critical applications with categorical inputs. Previous research efforts on accessing model robustness against manipulation of categorical inputs are specific to use cases…

针对视觉基础模型的任务无关攻击

Task-Agnostic Attacks Against Vision Foundation Models

针对视觉基础模型在多下游任务中的安全盲区，提出一种通用框架，通过最大程度破坏特征表示来伪造任务无关的对抗样本，并广泛评估了其对多任务的影响及模型间的可迁移性。