Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security最新文献_第7页

POSTER: Toward Intelligent Cyber Attacks for Moving Target Defense Techniques in Software-Defined Networking 海报:面向软件定义网络中移动目标防御技术的智能网络攻击

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3592825

Tina Moghaddam, Guowei Yang, Chandra Thapa, S. Çamtepe, Dan Dongseong Kim

{"title":"POSTER: Toward Intelligent Cyber Attacks for Moving Target Defense Techniques in Software-Defined Networking","authors":"Tina Moghaddam, Guowei Yang, Chandra Thapa, S. Çamtepe, Dan Dongseong Kim","doi":"10.1145/3579856.3592825","DOIUrl":"https://doi.org/10.1145/3579856.3592825","url":null,"abstract":"Moving Target Defenses (MTD) are proactive security countermeasures that change the attack surface in a system in ways that make it harder for attackers to succeed. These techniques have been shown to be effective, and their application in software-defined networking (SDN) against simple automated attacks is growing in popularity. However, with the increased knowledge of and ease of access to Artificial Intelligence (AI) techniques, AI is starting to be used to enhance cyber attacks, which are becoming increasingly complex. Hence, the evaluation of MTDs against simple automated attacks is no longer enough to demonstrate their effectiveness in increasing system security. With this in mind, we propose a novel framework to evaluate MTD techniques in SDN. To this end, first, we develop a taxonomy of possible intelligent attacks against MTD techniques. Second, we show how our framework can be used to generate datasets to realize these intelligent attacks for evaluating and enhancing MTD techniques. Third, we experimentally demonstrate the feasibility of the proposed machine learning (ML) powered attacks, with an attacker who can determine the MTD trigger time from network traffic using ML, which they can use to maximize their attack window and increase their chances of success.","PeriodicalId":156082,"journal":{"name":"Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128763400","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

Extracting Privacy-Preserving Subgraphs in Federated Graph Learning using Information Bottleneck 利用信息瓶颈提取联邦图学习中的隐私保护子图

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3595791

Chenhan Zhang, Wen Wang, James J. Q. Yu, Shui Yu

{"title":"Extracting Privacy-Preserving Subgraphs in Federated Graph Learning using Information Bottleneck","authors":"Chenhan Zhang, Wen Wang, James J. Q. Yu, Shui Yu","doi":"10.1145/3579856.3595791","DOIUrl":"https://doi.org/10.1145/3579856.3595791","url":null,"abstract":"As graphs are getting larger and larger, federated graph learning (FGL) is increasingly adopted, which can train graph neural networks (GNNs) on distributed graph data. However, the privacy of graph data in FGL systems is an inevitable concern due to multi-party participation. Recent studies indicated that the gradient leakage of trained GNN can be used to infer private graph data information utilizing model inversion attacks (MIA). Moreover, the central server can legitimately access the local GNN gradients, which makes MIA difficult to counter if the attacker is at the central server. In this paper, we first identify a realistic crowdsourcing-based FGL scenario where MIA from the central server towards clients’ subgraph structures is a nonnegligible threat. Then, we propose a defense scheme, Subgraph-Out-of-Subgraph (SOS), to mitigate such MIA and meanwhile, maintain the prediction accuracy. We leverage the information bottleneck (IB) principle to extract task-relevant subgraphs out of the clients’ original subgraphs. The extracted IB-subgraphs are used for local GNN training and the local model updates will have less information about the original subgraphs, which renders the MIA harder to infer the original subgraph structure. Particularly, we devise a novel neural network-powered approach to overcome the intractability of graph data’s mutual information estimation in IB optimization. Additionally, we design a subgraph generation algorithm for finally yielding reasonable IB-subgraphs from the optimization results. Extensive experiments demonstrate the efficacy of the proposed scheme, the FGL system trained on IB-subgraphs is more robust against MIA attacks with minuscule accuracy loss.","PeriodicalId":156082,"journal":{"name":"Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128815538","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

Formal Methods for Payment Protocols 支付协议的正式方法

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3596440

D. Basin

引用次数: 1

Arvin: Greybox Fuzzing Using Approximate Dynamic CFG Analysis 使用近似动态CFG分析的灰盒模糊

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3582813

Sirus Shahini, Mu Zhang, Mathias Payer, R. Ricci

{"title":"Arvin: Greybox Fuzzing Using Approximate Dynamic CFG Analysis","authors":"Sirus Shahini, Mu Zhang, Mathias Payer, R. Ricci","doi":"10.1145/3579856.3582813","DOIUrl":"https://doi.org/10.1145/3579856.3582813","url":null,"abstract":"Fuzzing has emerged as the most broadly used testing technique to discover bugs. Effective fuzzers rely on coverage to prioritize inputs that exercise new program areas. Edge-based code coverage of the Program Under Test (PUT) is the most commonly used coverage today. It is cheap to collect—a simple counter per basic block edge suffices. Unfortunately, edge coverage lacks context information: it exclusively records how many times each edge was executed but lacks the information necessary to trace actual paths of execution. Our new fuzzer Arvin gathers probabilistic full traces of PUT executions to construct Dynamic Control Flow Graphs (DCFGs). These DCFGs observe a richer set of program behaviors, such as the \"depth\" of execution, different paths to reach the same basic block, and targeting specific functions and paths. Prioritizing the most promising inputs based on these behaviors improves fuzzing effectiveness by increasing the diversity of explored basic blocks. Designing a DCFG-aware fuzzer raises a key challenge: collecting the required information needs complex instrumentation which results in performance overheads. Our prototype approximates DCFG and enables lightweight, asynchronous coordination between fuzzing processes, making DCFG-based fuzzing practical. By approximating DCFGs, Arvin is fast, resulting in at least an eight-fold increase in fuzzing speed. Because it effectively prioritizes inputs using methods like depth comparison and directed exclusion, which are unavailable to other fuzzers, it finds bugs missed by others. We compare its ability to find bugs using various Linux programs and discover 50 bugs, 23 of which are uniquely found by Arvin.","PeriodicalId":156082,"journal":{"name":"Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127860905","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

Symbolic modelling of remote attestation protocols for device and app integrity on Android Android设备和应用完整性远程认证协议的符号建模

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3582812

Abdulla Aldoseri, Tom Chothia, José Moreira, David F. Oswald

{"title":"Symbolic modelling of remote attestation protocols for device and app integrity on Android","authors":"Abdulla Aldoseri, Tom Chothia, José Moreira, David F. Oswald","doi":"10.1145/3579856.3582812","DOIUrl":"https://doi.org/10.1145/3579856.3582812","url":null,"abstract":"Ensuring the integrity of a remote app or device is one of the most challenging concerns for the Android ecosystem. Software-based solutions provide limited protection and can usually be circumvented by repacking the mobile app or rooting the device. Newer protocols use trusted hardware to provide stronger remote attestation guarantees, e.g., Google SafetyNet, Samsung Knox (V2 and V3 attestation), and Android Key Attestation. So far, the protocols used by these systems have received relatively little attention. In this paper, we formally model these platforms using the Tamarin Prover and verify their security properties in the symbolic model of cryptography, revealing two vulnerabilities: we found a relay attack against Samsung Knox V2 that allows a malicious app to masquerade as an honest app, and an error in the recommended use case for Android Key Attestation that means that old—possibly out of date—attestations can be replayed. We employed our findings and the modelled platforms to tackle one of the most challenging problems in Android security, namely code protection, proposing and formally modelling a code protection scheme that ensures source code protection for mobile apps using a hardware root of trust.","PeriodicalId":156082,"journal":{"name":"Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130724817","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

Implementing and Optimizing Matrix Triples with Homomorphic Encryption 矩阵三元组的同态加密实现与优化

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3590344

Johannes Mono, T. Güneysu

{"title":"Implementing and Optimizing Matrix Triples with Homomorphic Encryption","authors":"Johannes Mono, T. Güneysu","doi":"10.1145/3579856.3590344","DOIUrl":"https://doi.org/10.1145/3579856.3590344","url":null,"abstract":"In today’s interconnected world, data has become a valuable asset, leading to a growing interest in protecting it through techniques such as privacy-preserving computation. Two well-known approaches are multi-party computation and homomorphic encryption with use cases such as privacy-preserving machine learning evaluating or training neural networks. For multi-party computation, one of the fundamental arithmetic operations is the secure multiplication in the malicious security model and by extension the multiplication of matrices which is expensive to compute in the malicious model. Transferring the problem of secure matrix multiplication to the homomorphic domain enables savings in communication complexity, reducing the main bottleneck. In this work, we implement and optimize the homomorphic generation of matrix triples. We provide an open-source implementation for the leveled BGV (Brakerski Gentry Vaikuntanathan) scheme supporting plaintext moduli of arbitrary size using state-of-the-art implementation techniques. We also provide a new, use-case specific approach to parameter generation for leveled BGV-like schemes heuristically optimizing for computation time and taking into account architecture-specific constraints. Finally, we provide an in-depth analysis of the homomorphic circuit enabling the re-use of key switching keys and eliminating constant multiplications, combining our results in an implementation to generate homomorphic matrix triples for arbitrary plaintext moduli. Our implementation is publicly available and up to 2.1 × faster compared to previous work while also providing new time-memory trade-offs for different computing environments. Furthermore, we implement and evaluate additional, use-case specific optimization opportunities such as matrix slicing for the matrix triple generation.","PeriodicalId":156082,"journal":{"name":"Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122812171","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

POSTER: Security Logs Graph Analytics for Industry Network System 海报:工业网络系统安全日志图分析

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3592830

Qiaoran Meng, Nay Oo, Hoontae Lim, B. Sikdar

引用次数: 0

Flag: A Framework for Lightweight Robust Secure Aggregation 标志:一个轻量级鲁棒安全聚合框架

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3595805

Laasya Bangalore, Mohammad Hossein Faghihi Sereshgi, Carmit Hazay, Muthuramakrishnan Venkitasubramaniam

引用次数: 0

Secure and Efficient Mobile DNN Using Trusted Execution Environments 使用可信执行环境的安全高效移动DNN

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3582820

B. Hu, Yan Wang, Jerry Q. Cheng, Tianming Zhao, Yucheng Xie, Xiaonan Guo, Ying Chen

{"title":"Secure and Efficient Mobile DNN Using Trusted Execution Environments","authors":"B. Hu, Yan Wang, Jerry Q. Cheng, Tianming Zhao, Yucheng Xie, Xiaonan Guo, Ying Chen","doi":"10.1145/3579856.3582820","DOIUrl":"https://doi.org/10.1145/3579856.3582820","url":null,"abstract":"Many mobile applications have resorted to deep neural networks (DNNs) because of their strong inference capabilities. Since both input data and DNN architectures could be sensitive, there is an increasing demand for secure DNN execution on mobile devices. Towards this end, hardware-based trusted execution environments on mobile devices (mobile TEEs), such as ARM TrustZone, have recently been exploited to execute CNN securely. However, running entire DNNs on mobile TEEs is challenging as TEEs have stringent resource and performance constraints. In this work, we develop a novel mobile TEE-based security framework that can efficiently execute the entire DNN in a resource-constrained mobile TEE with minimal inference time overhead. Specifically, we propose a progressive pruning to gradually identify and remove the redundant neurons from a DNN while maintaining a high inference accuracy. Next, we develop a memory optimization method to deallocate the memory storage of the pruned neurons utilizing the low-level programming technique. Finally, we devise a novel adaptive partitioning method that divides the pruned model into multiple partitions according to the available memory in the mobile TEE and loads the partitions into the mobile TEE separately with a minimal loading time overhead. Our experiments with various DNNs and open-source datasets demonstrate that we can achieve 2-30 times less inference time with comparable accuracy compared to existing approaches securing entire DNNs with mobile TEE.","PeriodicalId":156082,"journal":{"name":"Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128710229","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

FlushTime: Towards Mitigating Flush-based Cache Attacks via Collaborating Flush Instructions and Timers on ARMv8-A FlushTime:通过在ARMv8-A上协作Flush指令和计时器来减轻基于Flush的缓存攻击

Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security Pub Date : 2023-07-10 DOI: 10.1145/3579856.3595803

Jingquan Ge, Fengwei Zhang

{"title":"FlushTime: Towards Mitigating Flush-based Cache Attacks via Collaborating Flush Instructions and Timers on ARMv8-A","authors":"Jingquan Ge, Fengwei Zhang","doi":"10.1145/3579856.3595803","DOIUrl":"https://doi.org/10.1145/3579856.3595803","url":null,"abstract":"ARMv8-A processors generally utilize optimization techniques such as multi-layer cache, out-of-order execution and branch prediction to improve performance. These optimization techniques are inevitably threatened by cache-related attacks including Flush+Reload, Flush+Flush, Meltdown, Spectre, and their variants. These attacks can break the isolation boundaries between different processes or even between user and kernel spaces. Researchers proposed many defense schemes to resist these cache-related attacks. However, they either need to modify the hardware architecture, have incomplete coverage, or introduce significant performance overhead. In this paper, we propose FlushTime, a more secure collaborative framework of cache flush instructions and generic timer on ARMv8-A. Based on the instruction/register trap mechanism of ARMv8-A, FlushTime traps cache flush instructions and generic timer from user space into kernel space, and makes them cooperate with each other in kernel space. When a flush instruction is called, the generic timer resolution will be reduced for several time slices. This collaborative mechanism can greatly mitigate the threat of all flush-based cache-related attacks. Since normal applications rarely need to obtain high resolution timestamps immediately after calling a flush instruction, FlushTime does not affect the normal operation of the system. Security and performance evaluations show that FlushTime can resist all flush-based cache-related attacks while introducing an extremely low performance overhead.","PeriodicalId":156082,"journal":{"name":"Proceedings of the 2023 ACM Asia Conference on Computer and Communications Security","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128361523","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