2021 IEEE Symposium on Computers and Communications (ISCC)最新文献_第10页

Tripod: Use Data Augmentation to Enhance Website Fingerprinting 三脚架:使用数据增强来增强网站指纹

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631528

Yixi Zhang, Xueliang Sun, Xiang Qin, Chaoran Li, Siwei Wang, Yi Xie

{"title":"Tripod: Use Data Augmentation to Enhance Website Fingerprinting","authors":"Yixi Zhang, Xueliang Sun, Xiang Qin, Chaoran Li, Siwei Wang, Yi Xie","doi":"10.1109/ISCC53001.2021.9631528","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631528","url":null,"abstract":"Website Fingerprinting (WF) enables a passive adversary to identify the website a user is visiting, even when the web access adopts security or privacy technologies. WF attacks based on deep learning are highly effective when feeding sufficient training traces, for example, hundreds of traffic traces of accessing each website. However, collecting extensive traffic consumes much time and resources, degenerating WF attacks' timeliness and invisibility. Nevertheless, decreasing training traces dramatically drops the WF accuracy. This paper proposes Tripod, a novel data augmentation method to enhance WF attacks, making them effective with a small training set. It applies three packet manipulations (Injecting, Removing, and Losing) on one collected traffic trace to generate several augmented traces. WF attacks then use the website classifier trained by the augmented set of all traces. In the closed-world scenario, the Var-CNN attack with 20 training traces per website only correctly identifies 56.1% of websites, while Tripod significantly increases this accuracy to 95.9%. Furthermore, Tripod increases the true positive rate of Var-CNN from 26.9% to 91.4% in the more realistic open-world scenario.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"133 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125799154","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

Mobile Augmented Reality for Craniotomy Planning 用于开颅手术计划的移动增强现实

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631438

M. Alves, Daniel Oliveira Dantas

引用次数: 2

Danian: tail latency reduction of networking application through an O(1) scheduler Danian:通过O(1)调度器减少网络应用程序的尾部延迟

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631451

Gustavo Pantuza, Lucas A. C. Bleme, M. Vieira, L. Vieira

引用次数: 2

Deep Quantile Regression for QoT Inference and Confident Decision Making QoT推理与自信决策的深度分位数回归

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631468

T. Panayiotou, Hafsa Maryam, G. Ellinas

引用次数: 2

Scalability of Hash-Based Pattern Matching for High-Speed Network Security and Monitoring 高速网络安全与监控中基于哈希模式匹配的可扩展性

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631447

Tomás Fukac, J. Korenek, J. Matoušek

{"title":"Scalability of Hash-Based Pattern Matching for High-Speed Network Security and Monitoring","authors":"Tomás Fukac, J. Korenek, J. Matoušek","doi":"10.1109/ISCC53001.2021.9631447","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631447","url":null,"abstract":"Gradually increasing throughput of high-speed networks puts continuous pressure on the performance of operations over a stream of network data. Probably the most affected operation in the area of network security and monitoring is pattern matching, which is in the core of widely deployed intrusion detection systems (IDSes) like Snort, Suricata and Bro. This paper therefore proposes several optimizations of a hash-based pattern matching architecture that together allow to increase its throughput to 100 Gbps and beyond. Proposed optimizations target an interconnection network between parallel hash function engines and independent memory blocks addressed by hashes computed over short strings of input data. Specifically, the optimizations reduce resource utilization by sharing parts of the full interconnection network among its several outputs and lower collision rate in these shared parts by aggregation and distributed buffering of memory access requests. The optimized pattern matching architecture is therefore able to utilize a higher number of parallel hash functions, each of which can use the interconnection network to access any memory block. This allows not only to increase the throughput of a key component within IDSes to more than 100 Gbps, but also to support a larger set of network threat patterns and to update this set dynamically.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132998544","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

A Self-Sovereign Decentralized Identity Platform Based on Blockchain 基于区块链的自主去中心化身份平台

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631518

Ya Chen, Chao Liu, Yu Wang, Yazhe Wang

引用次数: 4

FastHorovod: Expediting Parallel Message-Passing Schedule for Distributed DNN Training fastthorovod:分布式DNN训练的加速并行消息传递调度

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631443

Yanghai Wang, Dezun Dong, Yemao Xu, Shuo Ouyang, Xiangke Liao

{"title":"FastHorovod: Expediting Parallel Message-Passing Schedule for Distributed DNN Training","authors":"Yanghai Wang, Dezun Dong, Yemao Xu, Shuo Ouyang, Xiangke Liao","doi":"10.1109/ISCC53001.2021.9631443","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631443","url":null,"abstract":"Large-scale deep neural networks training have been widely deployed on dense-GPU public cloud clusters. Intensive communication and synchronization cost for gradients and parameters is becoming the bottleneck of distributed deep learning training. Horovod is one of the most popular distributed communication frameworks to address the scale-out issue of deep learning training on GPU clusters. Existing public-cloud GPU datacenters, such as Amazon EC2 and Alibaba GPU cloud, are usually equipped with commodity high-speed Ethernet and TCP networking. In current vanilla Horovod, however, we observe that one GPU device is merely associated with at most one proxy communication process. The proxy process is responsible for dealing with all the communication operations of parameter all-reduce for one or multiple GPUs. Such configuration makes communication interface based on TCP protocols suffer from limited network goodput and incur training performance penalties. In this paper, we make the first attempt to improve the message passing interface of Horovod and address the mismatching between the computation and communication capability when deploying Horovod in TCP-based public-cloud GPU clusters. We propose FastHorovod to exploit more cost-efficient auxiliary communication processes on CPU to expedite parallel message-passing schedule for GPU. We conduct extensive experiments against state-of-the-art Horovod. The experiment results show that our design can significantly accelerate the distributed training communication on TCP-based public-cloud GPU clusters, and FastHorovod improves the training speed of AlexNet and VGG16 models by 64.5% and 72.6% respectively.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131336819","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

An Effective and Efficient Method for Word-Level Textual Adversarial Attack 一种有效的词级文本对抗性攻击方法

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631472

Zhixin Shi, Yuru Ma, Xiaoyan Yu

{"title":"An Effective and Efficient Method for Word-Level Textual Adversarial Attack","authors":"Zhixin Shi, Yuru Ma, Xiaoyan Yu","doi":"10.1109/ISCC53001.2021.9631472","DOIUrl":"https://doi.org/10.1109/ISCC53001.2021.9631472","url":null,"abstract":"Adversarial examples are used to reveal the vulnerability of deep neural networks (DNNs) and improve their robustness. The word-level attack is a well-studied class of textual adversarial attack methods. However, existing word-level attacks have unstable success rates in different application scenarios. And the attacks under black-box setting suffer from low efficiency because they need to query the target DNN model with a great quantity. In this paper, we present SynonymPSO, a word-level attack method for generating adversarial texts. Specifically, we use a variety of means to find and filter synonyms to construct a comprehensive candidate pool. Besides, we design a kind of modification record strategy to improve the efficiency of the particle swarm optimization algorithm. Compared with prior works, SynonymPSO has the following features: (1) effective - it outperforms the state-of-art attacks in terms of attack success rate on most occasions; (2) efficient - it generates adversarial examples with fewer queries and less time. We evaluate SynonymPSO on five datasets that belong to different text classification tasks, including sentiment analysis, natural language inference and spam detection. The experimental results demonstrate its effectiveness and efficiency. For instance, when attacking BiLSTM over Enron dataset, the attack success rate of our method is 20% higher than the baseline while the query number is reduced by 94%.","PeriodicalId":270786,"journal":{"name":"2021 IEEE Symposium on Computers and Communications (ISCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131365650","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

Hypertuming GRU Neural Networks for Edge Resource Usage Prediction 边缘资源使用预测的超调GRU神经网络

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631548

John Violos, Stylianos Tsanakas, T. Theodoropoulos, Aris Leivadeas, K. Tserpes, T. Varvarigou

引用次数: 3

Deep Reinforcement Learning-Based Routing Optimization Algorithm for Edge Data Center 基于深度强化学习的边缘数据中心路由优化算法

2021 IEEE Symposium on Computers and Communications (ISCC) Pub Date : 2021-09-05 DOI: 10.1109/ISCC53001.2021.9631254

Jixin Zhao, Shukui Zhang, Yang Zhang, Li Zhang, Hao Long

引用次数: 1