2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)最新文献_第2页

The Master and Parasite Attack 主人和寄生虫的攻击

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00029

Lukas Baumann, Elias Heftrig, Haya Shulman, M. Waidner

引用次数: 1

Revealing GPUs Vulnerabilities by Combining Register-Transfer and Software-Level Fault Injection 结合寄存器传输和软件级故障注入揭示gpu漏洞

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00042

F. Santos, J. E. R. Condia, L. Carro, M. Reorda, P. Rech

{"title":"Revealing GPUs Vulnerabilities by Combining Register-Transfer and Software-Level Fault Injection","authors":"F. Santos, J. E. R. Condia, L. Carro, M. Reorda, P. Rech","doi":"10.1109/DSN48987.2021.00042","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00042","url":null,"abstract":"The complexity of both hardware and software makes GPUs reliability evaluation extremely challenging. A low level fault injection on a GPU model, despite being accurate, would take a prohibitively long time (months to years), while software fault injection, despite being quick, cannot access critical resources for GPUs and typically uses synthetic fault models (e.g., single bit-flips) that could result in unrealistic evaluations. This paper proposes to combine the accuracy of Register- Transfer Level (RTL) fault injection with the efficiency of software fault injection. First, on an RTL GPU model (FlexGripPlus), we inject over 1.5 million faults in low-level resources that are unprotected and hidden to the programmer, and characterize their effects on the output of common instructions. We create a pool of possible fault effects on the operation output based on the instruction opcode and input characteristics. We then inject these fault effects, at the application level, using an updated version of a software framework (NVBitFI). Our strategy reduces the fault injection time from the tens of years an RTL evaluation would need to tens of hours, thus allowing, for the first time on GPUs, to track the fault propagation from the hardware to the output of complex applications. Additionally, we provide a more realistic fault model and show that single bit-flip injection would underestimate the error rate of six HPC applications and two convolutional neural networks by up to 48parcent (18parcent on average). The RTL fault models and the injection framework we developed are made available in a public repository to enable third-party evaluations and ease results reproducibility.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114764656","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: 18

Self-Healing Protocol: Repairing Schedules Online after Link Failures in Time-Triggered Networks 自修复协议:时间触发网络中链路故障后在线修复时间表

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00028

Francisco Pozo, G. Rodríguez-Navas, H. Hansson

引用次数: 1

Examining Failures and Repairs on Supercomputers with Multi-GPU Compute Nodes 在具有多gpu计算节点的超级计算机上检查故障和修复

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00043

Amir Taherin, Tirthak Patel, G. Georgakoudis, I. Laguna, Devesh Tiwari

引用次数: 2

An Application Agnostic Defense Against the Dark Arts of Cryptojacking 针对黑魔法加密的应用不可知防御

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00044

Nada Lachtar, Abdulrahman Abu Elkhail, Anys Bacha, Hafiz Malik

引用次数: 5

[Title page i] [标题页i]

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/dsn48987.2021.00001

引用次数: 0

InjectaBLE: Injecting malicious traffic into established Bluetooth Low Energy connections InjectaBLE:向已建立的低功耗蓝牙连接注入恶意流量

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00050

Romain Cayre, Florent Galtier, G. Auriol, V. Nicomette, M. Kaâniche, G. Marconato

{"title":"InjectaBLE: Injecting malicious traffic into established Bluetooth Low Energy connections","authors":"Romain Cayre, Florent Galtier, G. Auriol, V. Nicomette, M. Kaâniche, G. Marconato","doi":"10.1109/DSN48987.2021.00050","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00050","url":null,"abstract":"Bluetooth Low Energy (BLE) is nowadays one of the most popular wireless communication protocols for Internet of Things (IoT) devices. As a result, several attacks have targeted this protocol or its implementations in recent years, illustrating the growing interest for this technology. However, some major challenges remain from an offensive perspective, such as injecting arbitrary frames, hijacking the Slave role or performing a Manin-The-Middle in an already established connection. In this paper, we describe a novel attack called InjectaBLE, allowing to inject malicious traffic into an existing connection. This attack is highly critical as the vulnerability exploited is inherent to the BLE specification itself, which means that any BLE connection can be possibly vulnerable, regardless of the BLE devices involved in the connection. We describe the theoretical foundations of the attack, how to implement it in practice, and we explore four critical attack scenarios allowing to maliciously trigger a specific feature of the target device, hijack the Slave and Master role or to perform a Man-in-the-Middle attack. Finally, we discuss the impact of this attack and outline some mitigation measures.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133888900","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: 4

Test-of-Time Award 时间的考验奖

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/dsn48987.2021.00014

引用次数: 0

Time-Window Based Group-Behavior Supported Method for Accurate Detection of Anomalous Users 基于时间窗的群体行为支持的异常用户准确检测方法

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00038

Lun-Pin Yuan, Euijin Choo, Ting Yu, Issa M. Khalil, Sencun Zhu

{"title":"Time-Window Based Group-Behavior Supported Method for Accurate Detection of Anomalous Users","authors":"Lun-Pin Yuan, Euijin Choo, Ting Yu, Issa M. Khalil, Sencun Zhu","doi":"10.1109/DSN48987.2021.00038","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00038","url":null,"abstract":"Autoencoder-based anomaly detection methods have been used in identifying anomalous users from large-scale enterprise logs with the assumption that adversarial activities do not follow past habitual patterns. Most existing approaches typically build models by reconstructing single-day and individual-user behaviors. However, without capturing long-term signals and group-correlation signals, the models cannot identify low-signal yet long-lasting threats, and will wrongly report many normal users as anomalies on busy days, which, in turn, lead to high false positive rate. In this paper, we propose ACOBE, an Anomaly detection method based on COmpound BEhavior, which takes into consideration long-term patterns and group behaviors. ACOBE leverages a novel behavior representation and an ensemble of deep autoencoders and produces an ordered investigation list. Our evaluation shows that ACOBE outperforms prior work by a large margin in terms of precision and recall, and our case study demonstrates that ACOBE is applicable in practice for cyberattack detection.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124680478","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: 4

Catch You With Cache: Out-of-VM Introspection to Trace Malicious Executions 抓住你的缓存:虚拟机外自省跟踪恶意执行

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00045

Chao Su, Xuhua Ding, Qingkai Zeng

{"title":"Catch You With Cache: Out-of-VM Introspection to Trace Malicious Executions","authors":"Chao Su, Xuhua Ding, Qingkai Zeng","doi":"10.1109/DSN48987.2021.00045","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00045","url":null,"abstract":"Out-of-VM introspection is an imperative part of security analysis. The legacy methods either modify the system, introducing enormous overhead, or rely heavily on hardware features, which are neither available nor practical in most cloud environments. In this paper, we propose a novel analysis method, named as Catcher, that utilizes CPU cache to perform out-of-VM introspection. Catcher does not make any modifications to the target program and its running environment, nor demands special hardware support. Implemented upon Linux KVM, it natively introspects the target’s virtual memory. More importantly, it uses the cache-based side channel to infer the target control flow. To deal with the inherent limitations of the side channel, we propose several heuristics to improve the accuracy and stability of Catcher. Our experiments against various malware armored with packing techniques show that Catcher can recover the control flow in real time with around 67% to 97% accuracy scores. Catcher incurs a negligible overhead to the system and can be launched at anytime to monitor an ongoing attack inside a virtual machine.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117276488","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