2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)最新文献

Malware Family Characterization with Recurrent Neural Network and GHSOM Using System Calls 使用系统调用的递归神经网络和GHSOM的恶意软件家族表征

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00051

Shun-Wen Hsiao, Fang Yu

{"title":"Malware Family Characterization with Recurrent Neural Network and GHSOM Using System Calls","authors":"Shun-Wen Hsiao, Fang Yu","doi":"10.1109/CloudCom2018.2018.00051","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00051","url":null,"abstract":"Nowadays, a massive amount of sensitive data which are accessible and connected through personal computers and cloud services attract hackers to develop malicious software (malware) to steal them. Owing to the success of deep learning on image and language recognition, researchers direct security systems to analyze and identify malware with deep learning approaches. This paper addresses the problem of analyzing and identifying complex and unstructured malware behaviors by proposing a framework of combining unsupervised and supervised learning algorithms with a novel sequence-aware encoding method. Particularly, a hybrid GHSOM (the Growing Hierarchical Self-Organizing Map) algorithm is proposed to cluster and encode similar malware behavior sequences from system call sequences to clustering feature vectors. Then, a Recurrent Neural Network (RNN) is trained to detect malware and predict their corresponding malware families based on the sequence of the behavior vectors. Our experiments show that the accuracy rate can be up to 0.98 in malware detection and 0.719 in malware classification of an 18-category malware dataset.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123291690","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: 5

Message from the ADON 2018 Workshop Organizers 来自ADON 2018研讨会组织者的信息

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/cloudcom2018.2018.00013

引用次数: 0

Improving Tail Latency of Stateful Cloud Services via GC Control and Load Shedding 通过GC控制和减载改善有状态云服务的尾部延迟

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00034

Daniel Fireman, João Brunet, R. Lopes, David Quaresma, T. Pereira

{"title":"Improving Tail Latency of Stateful Cloud Services via GC Control and Load Shedding","authors":"Daniel Fireman, João Brunet, R. Lopes, David Quaresma, T. Pereira","doi":"10.1109/CloudCom2018.2018.00034","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00034","url":null,"abstract":"Most of the modern cloud web services execute on top of runtime environments like .NET's Common Language Runtime or Java Runtime Environment. On the one hand, runtime environments provide several off-the-shelf benefits like code security and cross-platform execution. On the other hand, runtime's features such as just-in-time compilation and automatic memory management add a non-deterministic overhead to the overall service time, increasing the tail of the latency distribution. In this context, the Garbage Collector (GC) is among the leading causes of high tail latency. To tackle this problem, we developed the Garbage Collector Control Interceptor (GCI) - a request interceptor algorithm, which is agnostic regarding the cloud service language, internals, and its incoming load. GCI is wholly decentralized and improves the tail latency of cloud services by making sure that service instances shed the incoming load while cleaning up the runtime heap. We evaluated GCI's effectiveness in a stateful service prototype, varying the number of available instances. Our results showed that using GCI eliminates the impact of the garbage collection on the service latency for small (4 nodes) and large (64 nodes) deployments with no throughput loss.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127013928","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

A Design of Blockchain-Based Architecture for the Security of Electronic Health Record (EHR) Systems 基于区块链的电子健康记录(EHR)系统安全架构设计

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00058

Guang Yang, Chunlei Li

引用次数: 58

Unsupervised Anomaly Event Detection for VNF Service Monitoring Using Multivariate Online Arima 基于多元在线Arima的VNF服务监测无监督异常事件检测

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00061

Florian Schmidt, Florian Suri-Payer, Anton Gulenko, Marcel Wallschläger, Alexander Acker, O. Kao

{"title":"Unsupervised Anomaly Event Detection for VNF Service Monitoring Using Multivariate Online Arima","authors":"Florian Schmidt, Florian Suri-Payer, Anton Gulenko, Marcel Wallschläger, Alexander Acker, O. Kao","doi":"10.1109/CloudCom2018.2018.00061","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00061","url":null,"abstract":"Cloud computing provides companies large scale access to virtual resources, offering cost efficient and flexible usage of digital resources at any time. Thus, companies digitalize their dedicated hardware solutions to virtualized services, which can run in a cloud environment. For example, telecommunication providers move their IP multimedia subsystems, which currently run on dedicated hardware, into the cloud. As the dedicated hardware solutions provided a reliability of 99.999% in the past, the same high reliability is demanded for the virtualized services. But these come with higher complexity due to the fragile computation stack and cannot provide such high requirements. Future zero touch administration systems can help to detect automatically anomalies, find root causes and execute automated remediation actions, providing, providing the opportunity to increase the reliability of the overall system. This work focusses on the detection of degraded state anomalies. We propose an unsupervised detection approach using a multivariate version of the Online Arima forecasting algorithm consuming real-time monitoring data. This approach is evaluated on a testbed running an open source implementation of the IP multimedia subsystem (Clearwater) executed on a replicated Openstack cloud. Results show the applicability of the Online Arima detection approach with high detection rates and low number of false alarms.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114539775","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

An Architectural Framework for Serverless Edge Computing: Design and Emulation Tools 无服务器边缘计算的体系结构框架:设计和仿真工具

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00024

C. Cicconetti, M. Conti, A. Passarella

引用次数: 14

QuantiC: Distance Metrics for Evaluating Multi-Tenancy Threats in Public Cloud 定量:公共云中评估多租户威胁的距离度量

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00042

Taous Madi, Mengyuan Zhang, Yosr Jarraya, Amir Alimohammadifar, M. Pourzandi, Lingyu Wang, M. Debbabi

引用次数: 8

Video Quality Prediction Under Time-Varying Loads 时变负载下的视频质量预测

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00035

Obinna Izima, R. Fréin, M. Davis

{"title":"Video Quality Prediction Under Time-Varying Loads","authors":"Obinna Izima, R. Fréin, M. Davis","doi":"10.1109/CloudCom2018.2018.00035","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00035","url":null,"abstract":"We are on the cusp of an era where we can responsively and adaptively predict future network performance from network device statistics in the Cloud. To make this happen, regression-based models have been applied to learn mappings between the kernel metrics of a machine in a service cluster and service quality metrics on a client machine. The path ahead requires the ability to adaptively parametrize learning algorithms for arbitrary problems and to increase computation speed. We consider methods to adaptively parametrize regularization penalties, coupled with methods for compensating for the effects of the time-varying loads present in the system, namely load-adjusted learning. The time-varying nature of networked systems gives rise to the need for faster learning models to manage them; paradoxically, models that have been applied have not explicitly accounted for their time-varying nature. Consequently previous studies have reported that the learning problems were ill-conditioned -the practical, undesirable consequence of this is variability in prediction quality. Subset selection has been proposed as a solution. We highlight the short-comings of subset selection. We demonstrate that load-adjusted learning, using a suitable adaptive regularization function, outperforms current subset selection approaches by 10% and reduces computation.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132850804","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

SGX-FS: Hardening a File System in User-Space with Intel SGX SGX- fs:使用Intel SGX加固用户空间中的文件系统

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00027

Dorian Burihabwa, P. Felber, H. Mercier, V. Schiavoni

{"title":"SGX-FS: Hardening a File System in User-Space with Intel SGX","authors":"Dorian Burihabwa, P. Felber, H. Mercier, V. Schiavoni","doi":"10.1109/CloudCom2018.2018.00027","DOIUrl":"https://doi.org/10.1109/CloudCom2018.2018.00027","url":null,"abstract":"File systems have long benefited from hardware acceleration to improve their performance. In order to leverage such hardware capabilities, file systems rely on direct and trusted support from the underlying operating system. However, this assumes that the OS and the associated kernel drivers, which access the accelerators, are trustworthy. The recent introduction of the Intel software guard extensions (SGX) instruction set allows application developers to lift part of these assumptions, in conjunction with the widespread availability of these new extensions in mass-market CPUs. With SGX, programmers can design secure applications under a stronger adversarial model, such as a compromised OS or kernel module. Code executes inside enclaves and is protected from privileged processes, including the OS itself. This paper presents SGX-FS, a new user-space file system that leverages SGX data sealing capabilities for secure in-memory and persistent storage. It combines the FUSE framework with SGX to securely protect user data. In particular, SGX-FS efficiently encrypts and decrypts the application data within the enclaves. We fully implement an open-source SGX-FS prototype and evaluate its performance by means of a representative set of nano-and micro-benchmarks.","PeriodicalId":365939,"journal":{"name":"2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125429843","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: 10

PReT: A Tool for Automatic Phase-Based Regression Testing PReT:一个基于阶段的自动回归测试工具

2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom) Pub Date : 2018-12-01 DOI: 10.1109/CloudCom2018.2018.00062

Arnamoy Bhattacharyya, C. Amza

引用次数: 5