2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)最新文献_第2页

Drug Side Effects Data Representation and Full Spectrum Inferencing Using Knowledge Graphs in Intelligent Telehealth 基于知识图谱的智能远程医疗药物副作用数据表示与全谱推理

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCloud.2016.49

Saravanan Jayaraman, Lixin Tao, Keke Gai, Ning Jiang

{"title":"Drug Side Effects Data Representation and Full Spectrum Inferencing Using Knowledge Graphs in Intelligent Telehealth","authors":"Saravanan Jayaraman, Lixin Tao, Keke Gai, Ning Jiang","doi":"10.1109/CSCloud.2016.49","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.49","url":null,"abstract":"Drug side effects data contains important constraints about side-effects and conflict avoidance of component and compound drug. These are critically important in checking out prescriptions to avoid complications. Current drug data side effect representations in XML does not have a proper knowledge representation mechanism to clearly specify all kinds of dependencies among the drug components and drugs. Therefore Doctors and caregivers often rely on human interpretation to check prescriptions which can be error-prone. The recently introduced Web Ontology Language (OWL) based approach for medical drug side effects data representation still suffers from several shortcomings inherent to the OWL restrictions like using \"is-a\" relationship and usage of object property based workarounds losing the clarity and dynamic relationship building expected by domain experts to represent knowledge. The proposed model Drug-Side Effects Representation And Inferencing (D-SERI) built using Knowledge Graph (KG) and enhanced PaceJena shows that the proposed model allowsthe doctors and caregivers to derive dynamic information about side effects avoiding costly errors caused by human interpretation.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127635946","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: 7

Electricity Cost Management for Cloud Data Centers under Diverse Delay Constraints 多时延约束下云数据中心电力成本管理

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCLOUD.2016.17

Yuqi Fan, Yongfeng Xia, Yuheng Liu, Xiaohui Yuan

引用次数: 3

Sequence-Based Analysis of Static Probe Instrumentation Data for a VMM-Based Anomaly Detection System 基于vmm的异常检测系统静态探头仪表数据序列分析

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCloud.2016.51

A. W. Paundu, T. Okuda, Y. Kadobayashi, S. Yamaguchi

引用次数: 0

Efran (O): "Efficient Scalar Homomorphic Scheme on MapReduce for Data Privacy Preserving" Efran (O):“MapReduce数据隐私保护的高效标量同态方案”

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCloud.2016.10

Martin Konan, Wenyong Wang, Brighter Agyemang

{"title":"Efran (O): \"Efficient Scalar Homomorphic Scheme on MapReduce for Data Privacy Preserving\"","authors":"Martin Konan, Wenyong Wang, Brighter Agyemang","doi":"10.1109/CSCloud.2016.10","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.10","url":null,"abstract":"Privacy protection is one of most concerned issues in big data and cloud applications in the last decade. Thereby, mapreduce which is a programming scheme with an associated parallel implementation for processing and generating large data sets on the heart of cloud applications needs to be securely implemented. Thus the security of map workers' data (intermediate data) of mapreduce model must be well protected. But the traditional operations on ciphertexts were not applicable at the reduce stage. So to provide a secure mapreduce scheme, there is a paramount need to protect the data, as well as to allow specific types of computations to be carried out on encrypted intermediate data. Therefore some homomorphic based models have been proposed to address this issue, which could compute over encrypted data without decrypting it. However those existing schemes have to send their private encryption key to untrusted server (DGHV model) or key's parameters (Gen10 scheme by Gentry) which drastically leaks either the plaintext or information about the cryptosystem. In this paper, we propose a secure homomorphic model (FHE_SHCR algorithm) which efficiently retrieves ciphertexts (R_c) at reduce phase without passing any parameters or private key to untrusted server. Also for the efficiency of our solution in terms of computation cost and security analysis, we use a scalar homomorphic approach rather than applying blinding algorithm (probabilistic, polynomial-time algorithm) which is computationally expensive. Doing so, we efficiently achieve a probabilistic and improved security level through our model which is proved feasible.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"163 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122284421","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

Mobile App Collusions and Its Cyber Security Implications 移动应用程序串通及其网络安全影响

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCloud.2016.9

A. Arabo

引用次数: 2

Dynamic Android Malware Classification Using Graph-Based Representations 基于图表示的Android恶意软件动态分类

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCloud.2016.27

Lifan Xu, D. Zhang, Marco A. Alvarez, J. Morales, Xudong Ma, John Cavazos

{"title":"Dynamic Android Malware Classification Using Graph-Based Representations","authors":"Lifan Xu, D. Zhang, Marco A. Alvarez, J. Morales, Xudong Ma, John Cavazos","doi":"10.1109/CSCloud.2016.27","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.27","url":null,"abstract":"Malware classification for the Android ecosystem can be performed using a range of techniques. One major technique that has been gaining ground recently is dynamic analysis based on system call invocations recorded during the executions of Android applications. Dynamic analysis has traditionally been based on converting system calls into flat feature vectors and feeding the vectors into machine learning algorithms for classification. In this paper, we implement three traditional feature-vector-based representations for Android system calls. For each feature vector representation, we also propose a novel graph-based representation. We then use graph kernels to compute pair-wise similarities and feed these similarity measures into a Support Vector Machine (SVM) for classification. To speed up the graph kernel computation, we compress the graphs using the Compressed Row Storage format, and then we apply OpenMP to parallelize the computation. Experiments show that the graph-based representations are able to improve the classification accuracy over the corresponding feature-vector-based representations from the same input. Finally we show that different representations can be combined together to further improve classification accuracy.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124424338","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: 24

An Analysis of Information Security Event Managers 信息安全事件管理器分析

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCloud.2016.19

Kutub Thakur, Sandra Kopecky, Moath Nuseir, M. Ali, Meikang Qiu

{"title":"An Analysis of Information Security Event Managers","authors":"Kutub Thakur, Sandra Kopecky, Moath Nuseir, M. Ali, Meikang Qiu","doi":"10.1109/CSCloud.2016.19","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.19","url":null,"abstract":"The most effective security starts with real time visibility into all activity on all systems, networks, database and applications. In this paper the focus in on structured data however, some semi-structured and unstructured data is also explored. Whether the source is from network traffic, user activity, or the application user, any variation from normal of abnormal activity could indicate that a threat is imminent and that your data or infrastructure is at risk. In the last several years, there has been a disturbing trend in which attackers are innovating much faster than the defenders. There has been a commercialization of malware with attack kits available through underground forums for anyone who wants to perpetrate any variety of attacks. Large botnets are available for rent, allowing attackers to send spam or launch DDos (distributed denial-of-service) attacks. Many attackers reuse malware and command and control (C & C) and methods, adapting their products over time to keep ahead of the anti malware industry and security professionals. This paper surveys ESMs (Enterprise Security Managers) and cyber-attack case studies.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"2007 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129435657","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

R-Learning and Gaussian Process Regression Algorithm for Cloud Job Access Control 云作业访问控制的r -学习和高斯过程回归算法

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCloud.2016.15

Zhiping Peng, Delong Cui, Yuanjia Ma, Jianbin Xiong, Bo Xu, Weiwei Lin

引用次数: 2

Wormhole Detection in Secured BGP Networks 安全BGP网络中的虫洞检测

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCloud.2016.38

Youssef Gahi, J. Israr, M. Guennoun

{"title":"Wormhole Detection in Secured BGP Networks","authors":"Youssef Gahi, J. Israr, M. Guennoun","doi":"10.1109/CSCloud.2016.38","DOIUrl":"https://doi.org/10.1109/CSCloud.2016.38","url":null,"abstract":"A wormhole attack is a specific mechanism where two or more Autonomous Systems (ASes) coordinate to perform a black hole attack by exchanging secure BGP updates over a tunnel, signing route attestations for each other. Routing protocols generally choose route through a wormhole because it is, in general, the shortest route. This attack can redirect traffic through a chosen path that is compromised by the attacker. It can also significantly degrade the performance of the network. In this paper we present an approach to detecting coordinated wormhole attack by the validation of the path to detect any tunnel that may exist between two consecutive nodes in the AS-PATH. Similarly to SoBGP, we require that each AS signs and publishes its local topology through the topology certificate. The BGP speaker can then verify that the AS path is wormhole free by assembling local topologies in a global inter-AS topology map. We develop a metric that calculates the likelihood that two consecutive ASes in the AS-PATH are real neighbors in the AS graph. We demonstrate this approach by developing a wormhole detector where randomly chosen ASes are colluding to perform attacks according to a stochastic distribution model. We present experimental results from testing this algorithm in a controlled environment, demonstrating that it has a high detection rate. Our analysis shows that the detection algorithm is optimized for detecting long tunnels, i.e. tunnels that span over multiple ASes.","PeriodicalId":410477,"journal":{"name":"2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud)","volume":"450 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123364185","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

A Cluster-Based Intrusion Detection Framework for Monitoring the Traffic of Cloud Environments 基于集群的云环境流量监控入侵检测框架

2016 IEEE 3rd International Conference on Cyber Security and Cloud Computing (CSCloud) Pub Date : 2016-06-25 DOI: 10.1109/CSCloud.2016.43

Bo Li, Peng Liu, Li Lin

引用次数: 14