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

DSN 2023 Research Track Program Committee DSN 2023研究轨道计划委员会

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/dsn58367.2023.00009

引用次数: 0

Intrusion Injection for Virtualized Systems: Concepts and Approach 虚拟化系统的入侵注入:概念和方法

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/DSN58367.2023.00047

Charles F. Gonçalves, Nuno Antunes, M. Vieira

{"title":"Intrusion Injection for Virtualized Systems: Concepts and Approach","authors":"Charles F. Gonçalves, Nuno Antunes, M. Vieira","doi":"10.1109/DSN58367.2023.00047","DOIUrl":"https://doi.org/10.1109/DSN58367.2023.00047","url":null,"abstract":"Virtualization is drawing attention due to countless benefits, leaving Hypervisors with the paramount responsibility for performance, dependability, and security. However, while there are consolidated approaches to assessing the performance and dependability of virtualized systems, solutions to assess security are very limited. Key difficulties are evaluating the system in the presence of unknown attacks and vulnerabilities and comparing the security attributes of different systems and configurations when an intrusion occurs. In this paper, we propose a novel concept and approach of intrusion injection for virtualized environments, which consists of directly driving the system into the erroneous states that mimic the ones resulting from actual intrusions (in the same way errors are injected to mimic the effects of residual faults). We present a prototype capable of injecting erroneous states related to memory-corruption in the Xen Hypervisor to show that the concept and approach proposed here are feasible. The prototype is evaluated using publicly disclosed exploits across three different versions of Xen. Results show that our tool can inject erroneous states equivalent to those resulting from attacks that exploit existing vulnerabilities, even on versions where those vulnerabilities do not exist.","PeriodicalId":427725,"journal":{"name":"2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128439933","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

PASTA: Pragmatic Automated System-Theoretic Process Analysis 实用自动化系统理论过程分析

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/DSN58367.2023.00058

Jette Petzold, Jana Kreiß, R. V. Hanxleden

引用次数: 0

William C. Carter Award 威廉·c·卡特奖

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/dsn58367.2023.00010

引用次数: 0

Micro Replication 微复制

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/DSN58367.2023.00024

T. Distler, M. Eischer, Laura Lawniczak

引用次数: 0

Get Your Cyber-Physical Tests Done! Data-Driven Vulnerability Assessment of Robotic Aerial Vehicles 完成你的网络物理测试!数据驱动的无人机脆弱性评估

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/DSN58367.2023.00020

Aolin Ding, Matthew Chan, Amin Hass, N. Tippenhauer, Shiqing Ma, S. Zonouz

{"title":"Get Your Cyber-Physical Tests Done! Data-Driven Vulnerability Assessment of Robotic Aerial Vehicles","authors":"Aolin Ding, Matthew Chan, Amin Hass, N. Tippenhauer, Shiqing Ma, S. Zonouz","doi":"10.1109/DSN58367.2023.00020","DOIUrl":"https://doi.org/10.1109/DSN58367.2023.00020","url":null,"abstract":"The rapid growth of robotic aerial vehicles (RAVs) has attracted extensive interest in numerous public and civilian applications, from flying drones to quadrotors. Security of RAV systems is posting greater challenges as RAV controller software becomes more complex and exposes a growing attack surface. Memory isolation techniques, which virtually separate the memory space and conduct hardware-based memory access control, are believed to prevent the attacker from compromising the entire system by exploiting one memory vulnerability. In this paper, we propose Ares, a new variable-level vulnerability assessment framework to explore deeper bugs from a combined cyber-physical perspective. We present a data-driven method to illustrate that, despite state-of-the-art memory isolation efforts, RAV systems are still vulnerable to physics-aware data manipulation attacks. We augment RAV control states with intermediate state variables by tracing accessible control parameters and vehicle dynamics within the same isolated memory region. With this expanded state variable space, we apply multivariate statistical analysis to investigate inter-variable quantitative data dependencies and search for vulnerable state variables. Ares utilizes a reinforcement learning-based method to show how an attacker can exploit memory bugs and parameter defects in a legitimate memory view and elaborately craft adversarial variable values to disrupt a RAV's safe operations. We demonstrate the feasibility and capability of Ares on the widely-used ArduPilot RAV framework. Our extensive empirical evaluation shows that the attacker can leverage these vulnerable state variables to achieve various RAV failures during real-time operation, and even evade existing defense solutions.","PeriodicalId":427725,"journal":{"name":"2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124146582","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

Keynotes 主题演讲

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/dsn58367.2023.00014

引用次数: 0

How Different are the Cloud Workloads? Characterizing Large-Scale Private and Public Cloud Workloads 云工作负载有何不同?描述大规模私有云和公共云工作负载

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/DSN58367.2023.00055

Xiaoting Qin, Minghua Ma, Yuheng Zhao, Jue Zhang, Chao Du, Yudong Liu, Anjaly Parayil, Chetan Bansal, S. Rajmohan, Íñigo Goiri, Eli Cortez C. Vilarinho, Si Qin, Qingwei Lin, Dongmei Zhang

{"title":"How Different are the Cloud Workloads? Characterizing Large-Scale Private and Public Cloud Workloads","authors":"Xiaoting Qin, Minghua Ma, Yuheng Zhao, Jue Zhang, Chao Du, Yudong Liu, Anjaly Parayil, Chetan Bansal, S. Rajmohan, Íñigo Goiri, Eli Cortez C. Vilarinho, Si Qin, Qingwei Lin, Dongmei Zhang","doi":"10.1109/DSN58367.2023.00055","DOIUrl":"https://doi.org/10.1109/DSN58367.2023.00055","url":null,"abstract":"With the rapid development of cloud systems, an increasing number of service workloads are deployed in the private cloud and/or public cloud. Although large cloud providers such as Azure and Google have published workload traces in the past, prior work has not focused on analyzing and characterizing the differences between private and public cloud workloads in detail. Based on our experience working with Azure, one of the most widely used cloud platforms in the world, we find that the workload characteristics are different between the private and public cloud workloads. Specifically, compared with the public cloud workloads, the private cloud workloads tend to be more homogeneous in both deployment sizes and utilization patterns, more static with occasional bursts in deployment characteristics, and more region-agnostic regarding the sensitivity to deployed regions. Our findings gain several insights and implications on cloud management and motivate us to build a centralized workload knowledge base.","PeriodicalId":427725,"journal":{"name":"2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114522050","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

Targeted Privacy Attacks by Fingerprinting Mobile Apps in LTE Radio Layer LTE无线层指纹识别移动应用的针对性隐私攻击

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/DSN58367.2023.00035

Jaejong Baek, P. Soundrapandian, Sukwha Kyung, Ruoyu Wang, Yan Shoshitaishvili, Adam Doupé, Gail-Joon Ahn

引用次数: 0

NPTSN: RL-Based Network Planning with Guaranteed Reliability for In-Vehicle TSSDN NPTSN:基于rl的车载TSSDN可靠性保证网络规划

2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2023-06-01 DOI: 10.1109/DSN58367.2023.00019

Weijiang Kong, Majid Nabi, K. Goossens

{"title":"NPTSN: RL-Based Network Planning with Guaranteed Reliability for In-Vehicle TSSDN","authors":"Weijiang Kong, Majid Nabi, K. Goossens","doi":"10.1109/DSN58367.2023.00019","DOIUrl":"https://doi.org/10.1109/DSN58367.2023.00019","url":null,"abstract":"To achieve strict reliability goals with lower redundancy cost, Time-Sensitive Software-Defined Networking (TSSDN) enables run-time recovery for future in-vehicle networks. While the recovery mechanisms rely on network planning to establish reliability guarantees, existing network planning solutions are not suitable for TSSDN due to its domain-specific scheduling and reliability concerns. The sparse solution space and expensive reliability verification further complicate the problem. We propose NPTSN, a TSSDN planning solution based on deep Reinforcement Learning (RL). It represents the domain-specific concerns with the RL environment and constructs solutions with an intelligent network generator. The network generator iteratively proposes TSSDN solutions based on a failure analysis and trains a decision-making neural network using a modified actor-critic algorithm. Extensive performance evaluations show that NPTSN guarantees reliability for more test cases and shortens the decision trajectory compared to state-of-the-art solutions. It reduces the network cost by up to 6.8x in the performed experiments.","PeriodicalId":427725,"journal":{"name":"2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126189216","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