2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)最新文献_第6页

Analyzing the Performance of the Inter-Blockchain Communication Protocol 区块链间通信协议的性能分析

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-03-20 DOI: 10.1109/DSN58367.2023.00026

João Otávio Massari Chervinski, D. Kreutz, Xiwei Xu, Jiangshan Yu

{"title":"Analyzing the Performance of the Inter-Blockchain Communication Protocol","authors":"João Otávio Massari Chervinski, D. Kreutz, Xiwei Xu, Jiangshan Yu","doi":"10.1109/DSN58367.2023.00026","DOIUrl":"https://doi.org/10.1109/DSN58367.2023.00026","url":null,"abstract":"With the increasing demand for communication between blockchains, improving the performance of cross-chain communication protocols becomes an emerging challenge. We take a first step towards analyzing the limitations of cross-chain communication protocols by comprehensively evaluating Cosmos Network's Inter-Blockchain Communication Protocol. To achieve our goal we introduce a novel framework to guide empirical evaluations of cross-chain communication protocols. We implement an instance of our framework as a tool to evaluate the IBC protocol. Our findings highlight several challenges, such as high transaction confirmation latency, bottlenecks in the blockchain's RPC implementation and concurrency issues that hinder the scalability of the cross-chain message relayer. We also demonstrate how to reduce the time required to complete cross-chain transfers by up to 70% when submitting large amounts of transfers. Finally, we discuss challenges faced during deployment with the objective of contributing to the development and advancement of cross-chain communication.","PeriodicalId":427725,"journal":{"name":"2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125176405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

SecDDR: Enabling Low-Cost Secure Memories by Protecting the DDR Interface SecDDR:通过保护DDR接口实现低成本安全内存

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2022-09-01 DOI: 10.1109/DSN58367.2023.00016

Ali Fakhrzadehgan, Prakash Ramrakhyani, Moinuddin K. Qureshi, M. Erez

{"title":"SecDDR: Enabling Low-Cost Secure Memories by Protecting the DDR Interface","authors":"Ali Fakhrzadehgan, Prakash Ramrakhyani, Moinuddin K. Qureshi, M. Erez","doi":"10.1109/DSN58367.2023.00016","DOIUrl":"https://doi.org/10.1109/DSN58367.2023.00016","url":null,"abstract":"The security goals of cloud providers and users include memory confidentiality and integrity, which requires implementing replay attack protection (RAP). RAP can be achieved using integrity trees or mutually authenticated channels. Integrity trees incur significant performance overheads and are impractical for protecting large memories. Mutually authenticated channels have been proposed only for packetized memory interfaces that address only a very small niche domain, require fundamental changes to memory system architecture, and assume fully-trusted modules. We propose SecDDR, a low-cost RAP that targets direct-attached memories, like DDRx. SecDDR avoids memory-side data authentication, and thus, only adds a small amount of logic to memory components and does not change the underlying DDR protocol, making it practical for widespread adoption. In contrast to prior mutual authentication proposals, which require trusting the entire memory module, SecDDR targets untrusted modules by placing its limited security logic on the DRAM die (or package) of the ECC chip. Our evaluation shows that SecDDR performs within 1% of an encryption-only memory without RAP and that SecDDR provides 18.8% and 7.8% average performance improvements (up to 190.4% and 24.8%) relative to a 64-ary integrity tree and an authenticated channel, respectively.","PeriodicalId":427725,"journal":{"name":"2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"12 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120915794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Fabricated Flips: Poisoning Federated Learning without Data 虚构的翻转:没有数据的联邦学习

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2022-02-07 DOI: 10.1109/DSN58367.2023.00036

Jiyue Huang, Zilong Zhao, L. Chen, Stefanie Roos

{"title":"Fabricated Flips: Poisoning Federated Learning without Data","authors":"Jiyue Huang, Zilong Zhao, L. Chen, Stefanie Roos","doi":"10.1109/DSN58367.2023.00036","DOIUrl":"https://doi.org/10.1109/DSN58367.2023.00036","url":null,"abstract":"Attacks on Federated Learning (FL) can severely reduce the quality of the generated models and limit the usefulness of this emerging learning paradigm that enables on-premise decentralized learning. However, existing untargeted attacks are not practical for many scenarios as they assume that i) the attacker knows every update of benign clients, or ii) the attacker has a large dataset to locally train updates imitating benign parties. In this paper, we propose a data-free untargeted attack (DFA) that synthesizes malicious data to craft adversarial models without eavesdropping on the transmission of benign clients at all or requiring a large quantity of task-specific training data. We design two variants of DFA, namely DFA-R and DFA-G, which differ in how they trade off stealthiness and effectiveness. Specifically, DFA-R iteratively optimizes a malicious data layer to minimize the prediction confidence of all outputs of the global model, whereas DFA-G interactively trains a malicious data generator network by steering the output of the global model toward a particular class. Experimental results on Fashion-MNIST, Cifar-10, and SVHN show that DFA, despite requiring fewer assumptions than existing attacks, achieves similar or even higher attack success rate than state-of-the-art untargeted attacks against various state-of-the-art defense mechanisms. Concretely, they can evade all considered defense mechanisms in at least 50% of the cases for CIFAR-10 and often reduce the accuracy by more than a factor of 2. Consequently, we design REFD, a defense specifically crafted to protect against data-free attacks. REFD leverages a reference dataset to detect updates that are biased or have a low confidence. It greatly improves upon existing defenses by filtering out the malicious updates and achieves high global model accuracy.","PeriodicalId":427725,"journal":{"name":"2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"778 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133039249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Adversarial Robustness of Point Cloud Semantic Segmentation 点云语义分割的对抗鲁棒性研究

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-12-11 DOI: 10.1109/DSN58367.2023.00056

Jiacen Xu, Zhe Zhou, Boyuan Feng, Yufei Ding, Zhou Li

引用次数: 0

Don't Knock! Rowhammer at the Backdoor of DNN Models 别敲门!DNN模型的后门

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-10-14 DOI: 10.1109/DSN58367.2023.00023

M. Tol, Saad Islam, Andrew J. Adiletta, B. Sunar, Ziming Zhang

{"title":"Don't Knock! Rowhammer at the Backdoor of DNN Models","authors":"M. Tol, Saad Islam, Andrew J. Adiletta, B. Sunar, Ziming Zhang","doi":"10.1109/DSN58367.2023.00023","DOIUrl":"https://doi.org/10.1109/DSN58367.2023.00023","url":null,"abstract":"State-of-the-art deep neural networks (DNNs) have been proven to be vulnerable to adversarial manipulation and backdoor attacks. Backdoored models deviate from expected behavior on inputs with predefined triggers while retaining performance on clean data. Recent works focus on software simulation of backdoor injection during the inference phase by modifying network weights, which we find often unrealistic in practice due to restrictions in hardware. In contrast, in this work for the first time, we present an end-to-end backdoor injection attack realized on actual hardware on a classifier model using Rowhammer as the fault injection method. To this end, we first investigate the viability of backdoor injection attacks in real-life deployments of DNNs on hardware and address such practical issues in hardware implementation from a novel optimization perspective. We are motivated by the fact that vulnerable memory locations are very rare, device-specific, and sparsely distributed. Consequently, we propose a novel network training algorithm based on constrained optimization to achieve a realistic backdoor injection attack in hardware. By modifying parameters uniformly across the convolutional and fully-connected layers as well as optimizing the trigger pattern together, we achieve state-of-the-art attack performance with fewer bit flips. For instance, our method on a hardware-deployed ResNet-20 model trained on CIFAR-10 achieves over 89% test accuracy and 92% attack success rate by flipping only 10 out of 2.2 million bits.","PeriodicalId":427725,"journal":{"name":"2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121788175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Adaptive Webpage Fingerprinting from TLS Traces 自适应网页指纹从TLS痕迹

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2020-10-19 DOI: 10.1109/DSN58367.2023.00049

V. Mavroudis, Jamie Hayes

引用次数: 0

vWitness: Certifying Web Page Interactions with Computer Vision vWitness:用计算机视觉验证网页交互

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2020-07-31 DOI: 10.1109/DSN58367.2023.00048

Shuang He, Lianying Zhao, D. Lie

{"title":"vWitness: Certifying Web Page Interactions with Computer Vision","authors":"Shuang He, Lianying Zhao, D. Lie","doi":"10.1109/DSN58367.2023.00048","DOIUrl":"https://doi.org/10.1109/DSN58367.2023.00048","url":null,"abstract":"Web servers service client requests, some of which might cause the web server to perform security-sensitive operations (e.g. money transfer, voting). An attacker may thus forge or maliciously manipulate such requests by compromising a web client. Unfortunately, a web server has no way of knowing whether the client from which it receives a request has been compromised or not-current “best practice” defenses such as user authentication or network encryption cannot aid a server as they all assume web client integrity. To address this shortcoming, we propose vWitness, which “witnesses” the interactions of a user with a web page and certifies whether they match a specification provided by the web server, enabling the web server to know that the web request is user-intended. The main challenge that vWitness overcomes is that even benign clients introduce unpredictable variations in the way they render web pages. vWitness differentiates between these benign variations and malicious manipulation using computer vision, allowing it to certify to the web server that 1) the web page user interface is properly displayed 2) observed user interactions are used to construct the web request. Our vWitness prototype achieves compatibility with modern web pages, is resilient to adversarial example attacks and is accurate and performant-vWitness achieves 99.97% accuracy and adds 197ms of overhead to the entire interaction session in the average case.","PeriodicalId":427725,"journal":{"name":"2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114800419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0