2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)最新文献_第6页

SEC: Secure, Efficient, and Compatible Source Address Validation with Packet Tags 安全，高效，兼容的源地址验证与数据包标签

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391554

Xinyu Yang, Jiahao Cao, Mingwei Xu

{"title":"SEC: Secure, Efficient, and Compatible Source Address Validation with Packet Tags","authors":"Xinyu Yang, Jiahao Cao, Mingwei Xu","doi":"10.1109/IPCCC50635.2020.9391554","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391554","url":null,"abstract":"Spoofed traffic has been a great threat to the Internet. Tag-based inter-AS source address validation solutions show great effectiveness and high deployment incentives on filtering spoofed traffic. However, they fail to consider secure key negotiation for tags, efficient tag generation for network devices, and compatible tag placement for network functionalities. In this paper, we present SEC, a secure, efficient, and compatible source address validation scheme based on packet tags. We provide a secure key negotiation method and a lightweight tag generation algorithm for SEC considering hardware limitations of network devices. They can be easily implemented in network devices to filter spoofed packets while forwarding packets at approximately line rate. We also carefully place all tags into appropriate option fields in packet headers to guarantee the compatibility of network functionalities. We implement SEC in real programmable switches. Both theoretical analysis and experimental results show SEC can verify source addresses of packets in a secure, efficient, and compatible way.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130111249","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

Secure Decentralized Application Development of Blockchain-based Games 基于区块链的游戏安全去中心化应用开发

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391556

Natalia Trojanowska, M. Kedziora, Moataz Hanif, H. Song

{"title":"Secure Decentralized Application Development of Blockchain-based Games","authors":"Natalia Trojanowska, M. Kedziora, Moataz Hanif, H. Song","doi":"10.1109/IPCCC50635.2020.9391556","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391556","url":null,"abstract":"The purpose of this paper is to present Ethereum decentralized application development methodology with focus on security issues and its verification. We introduce key concepts that are related to developing decentralized applications and Crypto Collectibles games. Moreover, the requirements for blockchain projects were presented along with a selection of use case examples. The paper concerns the application design process issues, starting from the methodology used, going through the description of requirements and specification, ending up with the implementation. Finally, an overview of the issues associated with the security of Ethereum decentralized applications is presented. We compared guidelines from Ethereum Smart Contract Best Practices by ConsenSys, Smart Contract Security Verification Standard created by SecuRing, Decentralized Application Security Project introduced by NCC Group, Security Considerations from Solidity documentation, Ethereum Smart Contracts Security Recommendations from Guylando Knowledge Lists, and Smart Contract Weakness Classification and Test Cases. It was discussed which guideline should be followed and when should the verification take place, considering the life cycle of the application. The paper covers different security risks related to blockchain games along with examples of how vulnerabilities can arise, how they can be detected during security verification phase, and countermeasures to address them.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130930920","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

SEGIVE: A Practical Framework of Secure GPU Execution in Virtualization Environment SEGIVE:虚拟化环境下安全GPU执行的实用框架

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391574

Ziyang Wang, Fangyu Zheng, Jingqiang Lin, Guang Fan, Jiankuo Dong

{"title":"SEGIVE: A Practical Framework of Secure GPU Execution in Virtualization Environment","authors":"Ziyang Wang, Fangyu Zheng, Jingqiang Lin, Guang Fan, Jiankuo Dong","doi":"10.1109/IPCCC50635.2020.9391574","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391574","url":null,"abstract":"With the advancement of processor technology, general-purpose GPUs have become popular parallel computing accelerators in the cloud. However, designed for graphics rendering and high-performance computing, GPUs are born without sound security mechanisms. Consequently, the GPU-based service in the cloud is vulnerable to attacks from the potentially compromised guest OS as large amounts of sensitive code and data are offloaded directly to the unprotected GPUs.In this paper, we propose SEGIVE, a practical framework of secure GPU execution in the virtualization environment, which protects offloaded device code and data from disclosure or tampering by malicious guest OSes through the full life cycle of security-critical GPU applications. First, SEGIVE secures all the traffic transferred to GPUs with Intel SGX technology, including the users’ sensitive data and GPU binaries. Second, with various memory isolation mechanisms, SEGIVE enhances security in multi-user execution scenarios by sharing a GPU among multiple workloads, which avoids underutilization of device resources. Besides, SEGIVE requires no modifications to application source codes, the GPU architecture, or I/O interconnection to fulfill security principles, and thus almost all prevailing GPU-based applications can easily benefit from SEGIVE with little porting effort. We have implemented SEGIVE with KVM-QEMU on off-the-shelf NVIDIA GPUs and CPUs. Evaluation results show that with security-enhances, the performance of SEGIVE prototype is still competitive to the native execution on compute-intensive applications, especially for the public-key cryptography algorithm.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123975849","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: 3

FProbe: Detecting Stealthy DGA-based Botnets by Group Activities Analysis FProbe:通过群活动分析检测隐秘的基于dga的僵尸网络

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391539

Jiawei Sun, Yuan Zhou, Shupeng Wang, Lei Zhang, Junjiao Liu, Junteng Hou, Zhicheng Liu

{"title":"FProbe: Detecting Stealthy DGA-based Botnets by Group Activities Analysis","authors":"Jiawei Sun, Yuan Zhou, Shupeng Wang, Lei Zhang, Junjiao Liu, Junteng Hou, Zhicheng Liu","doi":"10.1109/IPCCC50635.2020.9391539","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391539","url":null,"abstract":"Nowadays, we have witnessed the rise of botnet malicious activities. These botnets, as expected, are launched by Domain generation algorithm (DGA) to evade detection. There is a growing concern that the artificially designed DGA detection features are being vulnerable to attackers, where any well-designed manipulations would evade these existing feature-based detection and even the more robust behavior-based detection. One common point of existing evasion for behavior detection is using domain names with low query rate. In this paper, we propose FProbe, a novel technology using co-occurrence matrix and relaxed clustering procedure, which performs excellent performance in the scene of detecting low query rate and multi-domain evasion. We use a simple intuition, that is, DGA queries have a strong correlation between temporal and spatial features, but these temporal and spatial correlations are not very synchronous. The FProbe uses the co-occurrence matrix, which is widely used in the field of product recommendation and word frequency co-occurrence, and use these unsupervised methods to cluster infected hosts. In particular, through this matrix, we can quickly and effectively locate infected hosts in the scene of low query rate, instead of discarding the domain for its high threshold. Then, we use the relax association rules of Frequent Sequence Tree to cluster related domain names, and use supervised learning to determine malicious clusters. The FProbe has been evaluated in the campus network (4000 active users in peak load hours) and ISP DNS traffic (one billion queries per hour). The experimental results ( 96.3% accuracy rate of 1.9% false positive on average) illustrate the efficiency and accuracy of FProbe.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125609129","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

Privacy Preserving Inference with Convolutional Neural Network Ensemble 基于卷积神经网络集成的隐私保护推理

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391544

Alexander Xiong, M. Nguyen, Andrew So, Tingting Chen

{"title":"Privacy Preserving Inference with Convolutional Neural Network Ensemble","authors":"Alexander Xiong, M. Nguyen, Andrew So, Tingting Chen","doi":"10.1109/IPCCC50635.2020.9391544","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391544","url":null,"abstract":"Machine Learning as a Service on cloud not only provides a solution to scale demanding workloads, but also allows broader accessibility for the utilization of trained deep neural networks. For example, in the medical field, cloud-based deep-learning assisted diagnoses can be life-saving, especially in developing areas where experienced doctors and domain expertise are lacking. However, preserving end-users' data privacy while using cloud service for deep learning is a challenge. Some recent works based on fully homomorphic encryption have enabled neural-network predictions on encrypted input data. In this paper, we further extend the capability of privacy preserving deep neural network inference, through a joint decision made by multiple deep neural network models on encrypted data, to address bias caused by unbalanced local training datasets. In particular, we design and implement a privacy preserving prediction method through an ensemble of convolutional neural networks. The extensive experiment results show that our method can achieve higher accuracy compared to individual models, and preserve the user data privacy at the same level. We also verify the time efficiency of our implementation.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125773958","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

Edge Computing Based Privacy-Preserving Data Aggregation Scheme in Smart Grid 基于边缘计算的智能电网隐私保护数据聚合方案

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-11-06 DOI: 10.1109/IPCCC50635.2020.9391567

Yuhao Kang, Songtao Guo, Pan Li, Yuanyuan Yang

{"title":"Edge Computing Based Privacy-Preserving Data Aggregation Scheme in Smart Grid","authors":"Yuhao Kang, Songtao Guo, Pan Li, Yuanyuan Yang","doi":"10.1109/IPCCC50635.2020.9391567","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391567","url":null,"abstract":"Smart grid is a highly intelligent power system integrating advanced communication technology, sensor measurement and automatic control technology, which is gradually replacing the traditional power grid. However, the smart grid faces challenges of balancing privacy, efficiency and functionality when processing massive amount of data. In this paper, a smart grid model based on edge computing paradigm is established, and then an efficient privacy-preserving multidimensional data aggregation scheme is proposed. This scheme adopts an improved identity-based signature algorithm and Paillier homomorphic cryptosystem to protect the privacy of users. In addition, super-increasing sequence is used in the proposed scheme to enable smart meters to report multiple types of data in a single reporting message, so that the Service Center (SC) can perform one-way analysis of variance on the data to provide users with more personalized services. Also the security analysis indicates that the proposed scheme works in protecting user’s electricity consumption privacy. Finally, performance analyses indicate that this scheme can effectively reduce the computational overhead.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125892491","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: 2

When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning 什么时候才算够?用递归神经网络进行射频机器学习的“刚刚好”决策

2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC) Pub Date : 2020-10-13 DOI: 10.1109/IPCCC50635.2020.9391569

M. Moore, IV WilliamH.Clark, PhD R. Michael Buehrer, PhD William C. Headley

{"title":"When is Enough Enough? \"Just Enough\" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning","authors":"M. Moore, IV WilliamH.Clark, PhD R. Michael Buehrer, PhD William C. Headley","doi":"10.1109/IPCCC50635.2020.9391569","DOIUrl":"https://doi.org/10.1109/IPCCC50635.2020.9391569","url":null,"abstract":"Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals. Additionally, recurrent neural networks typically process data on a sequential basis, enabling the potential for near real-time results. In this work, we investigate the novel usage of \"just enough\" decision making metrics for making decisions during inference based on a variable number of input received symbols. Since some signals are more complex than others, due to channel conditions, transmitter/receiver effects, etc., being able to dynamically utilize just enough of the received symbols to make a reliable decision allows for more efficient decision making in applications such as electronic warfare and dynamic spectrum sharing. To demonstrate the validity of this concept, four approaches to making \"just enough\" decisions are considered in this work and each are analyzed for their applicability to wireless communication machine learning applications.","PeriodicalId":226034,"journal":{"name":"2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134046093","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: 2