2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)最新文献_第5页

Asteria: Deep Learning-based AST-Encoding for Cross-platform Binary Code Similarity Detection Asteria:基于深度学习的跨平台二进制代码相似性检测ast编码

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00036

Shouguo Yang, Long Cheng, Yicheng Zeng, Zhe Lang, Hongsong Zhu, Zhiqiang Shi

{"title":"Asteria: Deep Learning-based AST-Encoding for Cross-platform Binary Code Similarity Detection","authors":"Shouguo Yang, Long Cheng, Yicheng Zeng, Zhe Lang, Hongsong Zhu, Zhiqiang Shi","doi":"10.1109/DSN48987.2021.00036","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00036","url":null,"abstract":"Binary code similarity detection is a fundamental technique for many security applications such as vulnerability search, patch analysis, and malware detection. There is an increasing need to detect similar code for vulnerability search across architectures with the increase of critical vulnerabilities in IoT devices. The variety of IoT hardware architectures and software platforms requires to capture semantic equivalence of code fragments in the similarity detection. However, existing approaches are insufficient in capturing the semantic similarity. We notice that the abstract syntax tree (AST) of a function contains rich semantic information. Inspired by successful applications of natural language processing technologies in sentence semantic understanding, we propose a deep learning-based AST-encoding method, named ASTERIA, to measure the semantic equivalence of functions in different platforms. Our method leverages the Tree-LSTM network to learn the semantic representation of a function from its AST. Then the similarity detection can be conducted efficiently and accurately by measuring the similarity between two representation vectors. We have implemented an open-source prototype of ASTERIA. The Tree-LSTM model is trained on a dataset with 1,022,616 function pairs and evaluated on a dataset with 95,078 function pairs. Evaluation results show that our method outperforms the AST-based tool Diaphora and the-state-of-art method Gemini by large margins with respect to the binary similarity detection. And our method is several orders of magnitude faster than Diaphora and Gemini for the similarity calculation. In the application of vulnerability search, our tool successfully identified 75 vulnerable functions in 5,979 IoT firmware images.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"1 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":"129810592","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: 26

Data-centric Reliability Management in GPUs 以数据为中心的gpu可靠性管理

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00040

Gurunath Kadam, E. Smirni

{"title":"Data-centric Reliability Management in GPUs","authors":"Gurunath Kadam, E. Smirni","doi":"10.1109/DSN48987.2021.00040","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00040","url":null,"abstract":"Graphics Processing Units (GPUs) have become the default choice of acceleration in a wide range of application domains. To keep up with computational demands, the GPU memory system is constantly being innovated from both the cache and DRAM perspectives. Such innovations can adversely affect GPU reliability and in fact, can lead to an increase in the number of multi-bit faults. To address this problem, we systematically study a wide range of GPGPU applications and find that usually, only a small percentage of data needs protection to increase application resilience. This data is highly accessed and shared (constitutes hot memory), which implies that faults in this space can often lead to incorrect application output. An in-depth analysis of application code shows that information of such data can be passed on to the hardware to guide low-overhead detection/correction schemes. In this vein, we developed low-overhead partial data replication schemes that exploit latency tolerance in GPUs. Overall, this data-centric approach dramatically improves GPGPU application resilience, with a minimal additional average performance overhead of 1.2% for detection-only and 3.4% for detection-and-correction.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"1 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":"130771979","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

Avis: In-Situ Model Checking for Unmanned Aerial Vehicles Avis:无人机的原位模型检验

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00057

Max G. Taylor, Haicheng Chen, Feng Qin, Christopher Stewart

{"title":"Avis: In-Situ Model Checking for Unmanned Aerial Vehicles","authors":"Max G. Taylor, Haicheng Chen, Feng Qin, Christopher Stewart","doi":"10.1109/DSN48987.2021.00057","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00057","url":null,"abstract":"Control firmware in unmanned aerial vehicles (UAVs) uses sensors to model and manage flight operations, from takeoff to landing to flying between waypoints. However, sensors can fail at any time during a flight. If control firmware mishandles sensor failures, UAVs can crash, fly away, or suffer other unsafe conditions. In-situ model checking finds sensor failures that could lead to unsafe conditions by systematically failing sensors. However, the type of sensor failure and its timing within a flight affect its manifestation, creating a large search space. We propose Avis, an in-situ model checker to quickly uncover UAV sensor failures that lead to unsafe conditions. Avis exploits operating modes, i.e., a label that maps software execution to corresponding flight operations. Widely used control firmware already support operating modes. Avis injects sensor failures as the control firmware transitions between modes – a key execution point where mishandled software exceptions can trigger unsafe conditions. We implemented Avis and applied it to ArduPilot and PX4. Avis found unsafe conditions 2.4X faster than Bayesian Fault Injection, the leading, state-of-theart approach. Within the current code base of ArduPilot and PX4, Avis discovered 10 previously unknown software bugs that lead to unsafe conditions. Additionally, we reinserted 5 known bugs that caused serious, unsafe conditions and Avis correctly reported all of them.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"1 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":"128447439","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: 9

ADAM-CS: Advanced Asynchronous Monotonic Counter Service 高级异步单调计数器服务

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-06-01 DOI: 10.1109/DSN48987.2021.00053

André Martin, Cong Lian, Franz Gregor, R. Krahn, V. Schiavoni, P. Felber, C. Fetzer

{"title":"ADAM-CS: Advanced Asynchronous Monotonic Counter Service","authors":"André Martin, Cong Lian, Franz Gregor, R. Krahn, V. Schiavoni, P. Felber, C. Fetzer","doi":"10.1109/DSN48987.2021.00053","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00053","url":null,"abstract":"Trusted execution environments (TEEs) offer the technological breakthrough to allow several applications to be deployed and executed over untrusted public cloud environments. Although TEEs (e. g., Intel SGX, ARM TrustZone, AMD SEV) provide several mechanisms to ensure confidentiality and integrity of data and code, they do not offer freshness out of the box, a critical aspect yet often overlooked, for instance, to protect against rollback attacks. Monotonic counters are a popular way to detect rollbacks, as their counter values cannot be decremented. However, counter increments are slow (i.e., 10th of milliseconds), making their use impractical for distributed services and applications processing thousands of transactions simultaneously, for which an order of magnitude improvement is needed. ADAM-CS is an asynchronous monotonic counter service to protect such high-traffic applications against rollback attacks. Leveraging a set of distributed monotonic counters and specific algorithms, ADAM-CS minimizes the maximum vulnerability window (MVW), i.e., the amount of transactions an adversary could successfully rollback. Thanks to its asynchronous nature, ADAM-CS supports thousands of increments per second without introducing additional latency in the transactions performed by applications. Our measurements indicate that we can keep the MVW well below 10ms while supporting a throughput of more than 21K requests/s when using eight counters.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"17 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":"116863137","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: 6

NVCache: A Plug-and-Play NVMM-based I/O Booster for Legacy Systems NVCache:即插即用的基于nvmm的传统系统I/O助推器

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-05-14 DOI: 10.1109/DSN48987.2021.00033

Rémi Dulong, Rafael Pires, Andreia Correia, V. Schiavoni, P. Ramalhete, P. Felber, Gaël Thomas

引用次数: 6

Plinius: Secure and Persistent Machine Learning Model Training Plinius:安全和持久的机器学习模型训练

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-04-07 DOI: 10.1109/DSN48987.2021.00022

Peterson Yuhala, P. Felber, V. Schiavoni, A. Tchana

{"title":"Plinius: Secure and Persistent Machine Learning Model Training","authors":"Peterson Yuhala, P. Felber, V. Schiavoni, A. Tchana","doi":"10.1109/DSN48987.2021.00022","DOIUrl":"https://doi.org/10.1109/DSN48987.2021.00022","url":null,"abstract":"With the increasing popularity of cloud based machine learning (ML) techniques there comes a need for privacy and integrity guarantees for ML data. In addition, the significant scalability challenges faced by DRAM coupled with the high access-times of secondary storage represent a huge performance bottleneck for ML systems. While solutions exist to tackle the security aspect, performance remains an issue. Persistent memory (PM) is resilient to power loss (unlike DRAM), provides fast and fine-granular access to memory (unlike disk storage) and has latency and bandwidth close to DRAM (in the order of ns and GB/s, respectively). We present PLINIUS, a ML framework using Intel SGX enclaves for secure training of ML models and PM for fault tolerance guarantees. PLINIUS uses a novel mirroring mechanism to create and maintain (i) encrypted mirror copies of ML models on PM, and (ii) encrypted training data in byte-addressable PM, for near-instantaneous data recovery after a system failure. Compared to disk-based checkpointing systems, PLINIUS is 3.2× and 3.7× faster respectively for saving and restoring models on real PM hardware, achieving robust and secure ML model training in SGX enclaves.","PeriodicalId":222512,"journal":{"name":"2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130290088","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: 11

Towards Optimal Use of Exception Handling Information for Function Detection 面向函数检测异常处理信息的优化利用

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-04-07 DOI: 10.1109/DSN48987.2021.00046

Chengbin Pang, Ruotong Yu, Dongpeng Xu, Eric Koskinen, G. Portokalidis, Jun Xu

引用次数: 7

Enabling Novel Interconnection Agreements with Path-Aware Networking Architectures 利用路径感知网络架构实现新型互联协议

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-04-06 DOI: 10.1109/DSN48987.2021.00027

Simon Scherrer, M. Legner, A. Perrig, S. Schmid

引用次数: 4

Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems 人工胰腺系统危害预测环境感知监测的数据驱动设计

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2021-04-06 DOI: 10.1109/DSN48987.2021.00058

Xugui Zhou, Bulbul Ahmed, J. Aylor, Philip Asare, H. Alemzadeh

引用次数: 11

Hiding in the Particles: When Return-Oriented Programming Meets Program Obfuscation 隐藏在粒子中:当面向返回的编程遇到程序混淆时

2021 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) Pub Date : 2020-12-11 DOI: 10.1109/DSN48987.2021.00064

Pietro Borrello, Emilio Coppa, Daniele Cono D'Elia

引用次数: 6