2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)最新文献_第6页

Network Anomaly Detection with Stochastically Improved Autoencoder Based Models 基于随机改进自编码器模型的网络异常检测

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.39

R. C. Aygun, A. Yavuz

引用次数: 107

Performance of Caffe on QCT Deep Learning Reference Architecture — A Preliminary Case Study Caffe在QCT深度学习参考架构上的性能——初步案例研究

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.49

V. Shankar, Stephen Chang

引用次数: 5

Malware Fingerprinting under Uncertainty 不确定性下的恶意软件指纹识别

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.63

Krishnendu Ghosh, W. Casey, J. Morales, B. Mishra

{"title":"Malware Fingerprinting under Uncertainty","authors":"Krishnendu Ghosh, W. Casey, J. Morales, B. Mishra","doi":"10.1109/CSCloud.2017.63","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.63","url":null,"abstract":"Malware detection and classification is critical for the security of IT infrastructure. Legacy detection of malware has been highly reliant on static signatures, so malware authors have evolved code polymorphic techniques to counteract these tools, thus rendering static malware detectors ineffective. While malware writers may easily use code rewriting techniques to scramble binary images; malware processes at runtime still must conduct a sequence of operational steps to achieve its design goal, indicating an approach based on behavioral analysis where the captured invariants form a new type of forensic fingerprint. Moreover these operational steps are constrained to occur within the computers' or mobile devices' abstract system interface - a finite basis of activities that submit to effective monitoring with a variety of tools. In this work, we propose a formalism for expressing these behaviors, learning them and analyzing them to form automated malware analysis tools. Thus motivated by a need to detect and classify malware, we root its foundation in formal verification, as well as methodology from statistical and machine learning. Specifically using trace data from malware we leverage formal verification methods (such as probabilistic model checking) to construct classifiers and evaluate their efficacy in supervised learning and cross-fold validation experiments. The results inform how a fully automated reasoning mechanism may be applied to unknown software by posing its system trace as a query to various classifiers as hypothesis testing, the outputs informing belief of membership. Finally, we demonstrate the method and results on real malware data.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129421526","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

Secure Framework for Future Smart City 未来智慧城市的安全框架

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.21

Hamza Djigal, Jun Feng, Jiamin Lu

{"title":"Secure Framework for Future Smart City","authors":"Hamza Djigal, Jun Feng, Jiamin Lu","doi":"10.1109/CSCloud.2017.21","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.21","url":null,"abstract":"With the recent advancements in the information and communication technologies, large number of devices are connecting to the Internet, hence large volumes of data in different formats and from different sources are generating. Consequently, on one hand dynamic and heterogeneous data sharing and management, in the ecosystem of Internet of Things (IoT), where every smart object is connected to Internet, presents new research challenges. On the other hand, citizen privacy preserving is another challenge, because he/she has to send his/her information to a service provider, to obtain the required information. This information is sensitive since it can reveal information about an individual. An attacker or a malicious service provider can utilize this sensitive information for their own business or something else. This paper presents a Secure Framework for Future Smart City (SEFSCITY), for better city living and governance, based on Cloud Computing IoT and Distributed Computing. We first present the architecture of SEFSCITY, which is based on Multi-Cloud and Cloud Federation approach; then we propose a security protocol for our framework. In our security model, we use Zero-Knowledge Protocol based on Elliptic Curve Discrete Logarithm Problem. Finally, we validate our architecture by conducting several scenarios that we have implemented using Cloud Analyst tool. The results show that in all scenarios, the cost infrastructure remains the same for the cloud customer, and our approach is benefic for the cloud provider in term of revenues and data processing time","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133962400","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

Unsupervised Labeling for Supervised Anomaly Detection in Enterprise and Cloud Networks 企业和云网络中监督异常检测的无监督标记

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.26

Sunhee Baek, Donghwoon Kwon, Jinoh Kim, S. Suh, Hyunjoo Kim, Ikkyun Kim

{"title":"Unsupervised Labeling for Supervised Anomaly Detection in Enterprise and Cloud Networks","authors":"Sunhee Baek, Donghwoon Kwon, Jinoh Kim, S. Suh, Hyunjoo Kim, Ikkyun Kim","doi":"10.1109/CSCloud.2017.26","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.26","url":null,"abstract":"Identifying anomalous events in the network is one of the vital functions in enterprises, ISPs, and datacenters to protect the internal resources. With its importance, there has been a substantial body of work for network anomaly detection using supervised and unsupervised machine learning techniques with their own strengths and weaknesses. In this work, we take advantage of the both worlds of unsupervised and supervised learning methods. The basic process model we present in this paper includes (i) clustering the training data set to create referential labels, (ii) building a supervised learning model with the automatically produced labels, and (iii) testing individual data points in question using the established learning model. By doing so, it is possible to construct a supervised learning model without the provision of the associated labels, which are often not available in practice. To attain this process, we set up a new property defining anomalies in the context of clustering, based on our observations from anomalous events in network, by which the referential labels can be obtained. Through our extensive experiments with a public data set (NSL-KDD), we will show that the presented method perform very well, yielding fairly comparable performance to the traditional method running with the original labels provided in the data set, with respect to the accuracy for anomaly detection.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127989354","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

Waveband Selection Based Feature Extraction Using Genetic Algorithm 基于波段选择的遗传算法特征提取

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.31

Yujun Li, Kun Liang, Xiaojun Tang, Keke Gai

引用次数: 4

An Improved Budget-Deadline Constrained Workflow Scheduling Algorithm on Heterogeneous Resources 一种改进的异构资源预算-截止日期约束工作流调度算法

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.8

Ting Sun, Chuangbai Xiao, Xiujie Xu, Guozhong Tian

引用次数: 1

Quality Check and Analysis of BeiDou and GPS Observation Data in the Experiment of Air-Gun in Reservoir 水库气枪试验中北斗与GPS观测数据的质量检验与分析

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.29

Ming-Quan Hong, Wen Zhao, Guang Chen, Chaoxian Chen, Ziliang Wang

{"title":"Quality Check and Analysis of BeiDou and GPS Observation Data in the Experiment of Air-Gun in Reservoir","authors":"Ming-Quan Hong, Wen Zhao, Guang Chen, Chaoxian Chen, Ziliang Wang","doi":"10.1109/CSCloud.2017.29","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.29","url":null,"abstract":"The next few years promises drastic improvements to global navigation satellite systems. USA is modernizing GPS, Russia is GLONASS, Europe is moving ahead with its own Galileo System, and China is expanding its BeiDou system from a regional navigation system to a full constellation global navigation satellite system known as BeiDou-2/Compass. Chinese BeiDou satellite navigation system constellation currently consists of twenty-six BeiDou satellites and can provide services of navigation and positioning in the Asia-Pacific Region. In this paper, we calculate the high frequency data of GPS and BeiDou by using the broadcast ephemeris, and the results are applied to the real-time positioning of the float platform in the experiment of Air-Gun in reservoir. We use the data to analyze the quality by multipath effect, signal noise ratio, positioning accuracy and so on. The results show that the accuracy of the BeiDou is slightly lower than that of GPS. The accuracy of GPS in horizontal direction is about 5 mm and that of vertical direction is about 12 mm, and the accuracy of BeiDou in horizontal direction is about 5.5 mm and that of vertical direction is about 16 mm.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131344778","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

Identifying Suspicious User Behavior with Neural Networks 用神经网络识别可疑用户行为

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.10

M. Ussath, David Jaeger, Feng Cheng, C. Meinel

{"title":"Identifying Suspicious User Behavior with Neural Networks","authors":"M. Ussath, David Jaeger, Feng Cheng, C. Meinel","doi":"10.1109/CSCloud.2017.10","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.10","url":null,"abstract":"The number of attacks that use sophisticated and complex methods increased lately. The main objective of these attacks is to largely infiltrate the target network and to stay undetected. Therefore, the attackers often use valid credentials and standard administrative tools to hide between legitimate user actions and to hinder detection. Most existing security systems, which use standard signature-based or anomaly-based approaches, are not able to identify this type of malicious activities. Furthermore, it is also most often not feasible to analyze user behavior manually, due to the complexity of this task and the high amount of different user actions. Thus, it is necessary to develop new automated approaches to identify suspicious user behavior. In this paper, we propose to use neural networks to analyze user behavior and to identify suspicious actions. Due to the fact that neural networks require suitable datasets to learn the difference between suspicious and benign actions, we describe a behavioral simulation system to generate reasonable datasets. These datasets use different behavioral features to describe log-on and log-off activities of users. To identify suitable neural network models for user behavior analysis, we evaluate and compare 16,275 different feed-forward neural networks with three different datasets and 75 recurrent neural networks with one dataset. The results show that the used dataset and the complexity of a model are crucial to achieve a high accuracy. Appropriate models, which also consider context behavior information, are able to automatically classify before unseen user actions with an accuracy of up to 98 %.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133307042","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: 21

SQLIIDaaS: A SQL Injection Intrusion Detection Framework as a Service for SaaS Providers SQLIIDaaS:面向SaaS提供商的SQL注入入侵检测框架服务

2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2017-06-01 DOI: 10.1109/CSCloud.2017.27

Mohamed Yassin, Hakima Ould-Slimane, C. Talhi, H. Boucheneb

{"title":"SQLIIDaaS: A SQL Injection Intrusion Detection Framework as a Service for SaaS Providers","authors":"Mohamed Yassin, Hakima Ould-Slimane, C. Talhi, H. Boucheneb","doi":"10.1109/CSCloud.2017.27","DOIUrl":"https://doi.org/10.1109/CSCloud.2017.27","url":null,"abstract":"Recently, we are attending to the proliferation of Cloud Computing (CC) as the new trending internet-based-Platform. Thanks to the outsourcing paradigm, CC is enabling many services. Software as a Service (SaaS) is one of those cloud-based-services. Indeed, SaaS model allows providers to reduce the cost of maintenance and management by transferring traditional on premise deployment to public Cloud. Clients can subscribe, in self-service, to SaaS services based on a pay-per-use model. However, since user data are outsourced to the Cloud, serious security breaches are rising and could harm the reputation of providers and slow down the subscription of clients. SQL injection attack (SQLIA) is one of the most critical SaaS vulnerabilities that allows attackers to violate the availability, confidentiality and integrity of user data. In this paper, we propose SQL injection intrusion detection framework as a service for SaaS providers, SQLIIDaaS, which allows a SaaS provider to detect SQLIAs targeting several SaaS applications without reading, analyzing or modifying the source code. To achieve SQL query/HTTP request mapping, we propose an event correlation based on the similarity between literals in SQL queries and parameters in HTTP requests. SQLIIDaaS is integrated and validated in Amazon Web Services (AWS). A SaaS provider can subscribe to this framework and launch its own set of virtual machines, which holds on-demand self-service, resource pooling, rapid elasticity, and measured service properties.","PeriodicalId":436299,"journal":{"name":"2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116327130","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: 8