Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847700
A. Furfaro, Teresa Gallo, A. Garro, D. Saccá, A. Tundis
This paper presents the practical exploitation of a goal-oriented methodology for requirements specification, called GOReM, for an application scenario involving the development of a cloud service offering a functionality of compliance analysis in the business model of Security as a Service (SecaaS). The requirements specification for this scenario emerged as a real need inside a large industrial project on the field of Cyber Security. GOReM has allowed to achieve in a lean, yet accurate, way the analysis of such a complex scenario, where non-functional requirements, coming from rules and regulations in force in different countries, complicate the handling of a cloud service which might be usable worldwide.
{"title":"Requirements specification of a cloud service for Cyber Security compliance analysis","authors":"A. Furfaro, Teresa Gallo, A. Garro, D. Saccá, A. Tundis","doi":"10.1109/CLOUDTECH.2016.7847700","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847700","url":null,"abstract":"This paper presents the practical exploitation of a goal-oriented methodology for requirements specification, called GOReM, for an application scenario involving the development of a cloud service offering a functionality of compliance analysis in the business model of Security as a Service (SecaaS). The requirements specification for this scenario emerged as a real need inside a large industrial project on the field of Cyber Security. GOReM has allowed to achieve in a lean, yet accurate, way the analysis of such a complex scenario, where non-functional requirements, coming from rules and regulations in force in different countries, complicate the handling of a cloud service which might be usable worldwide.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127189874","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}
Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847719
Safae Bouzbita, A. El Afia, R. Faizi, Mustapha Zbakh
The objective of the present paper is to propose an improved Ant Colony System (ACS) algorithm based on a Hidden Markov Model (HMM) so as dynamically adapt the local pheromone decay parameter ξ. The proposed algorithm uses Iteration and Diversity as indicators of the hidden states in the search space in ACS. To test the efficiency of our algorithm, we experimented it on several benchmark Travelling Salesman Problem (TSP) instances. The results have proven the effectiveness of our algorithm in both the convergence speed and the solution quality.
{"title":"Dynamic adaptation of the ACS-TSP local pheromone decay parameter based on the Hidden Markov Model","authors":"Safae Bouzbita, A. El Afia, R. Faizi, Mustapha Zbakh","doi":"10.1109/CLOUDTECH.2016.7847719","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847719","url":null,"abstract":"The objective of the present paper is to propose an improved Ant Colony System (ACS) algorithm based on a Hidden Markov Model (HMM) so as dynamically adapt the local pheromone decay parameter ξ. The proposed algorithm uses Iteration and Diversity as indicators of the hidden states in the search space in ACS. To test the efficiency of our algorithm, we experimented it on several benchmark Travelling Salesman Problem (TSP) instances. The results have proven the effectiveness of our algorithm in both the convergence speed and the solution quality.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126521250","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}
Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847720
A. Furfaro, A. Piccolo, D. Saccá, Andrea Parise
In the last few years, cyber security has become a hot topic because of the ever-increasing availability of Internet accessible services driven by the diffusion of connected devices. The consequent exposition to cyber threats demands for suitable methodologies, techniques and tools allowing to adequately handle issues arising in such a complex domain. We argue that the flexibility of virtual environments will play a critical role in many cyber security related aspects. Problems like the assessment of newly devised intrusion detection techniques, the evaluation of skills of cyber defense team members, the evaluation of the disruptive effects caused by the diffusion of new malware, are just few examples of issues that cannot be directly addressed in production systems even though they require realistic operating environments in order to be suitably performed. This paper describes the architecture of SMALLWORLD, a scalable software platform designed to reproduce realistic scenarios achieved by the immersion of real systems into a software defined virtual environment.
{"title":"A virtual environment for the enactment of realistic cyber security scenarios","authors":"A. Furfaro, A. Piccolo, D. Saccá, Andrea Parise","doi":"10.1109/CLOUDTECH.2016.7847720","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847720","url":null,"abstract":"In the last few years, cyber security has become a hot topic because of the ever-increasing availability of Internet accessible services driven by the diffusion of connected devices. The consequent exposition to cyber threats demands for suitable methodologies, techniques and tools allowing to adequately handle issues arising in such a complex domain. We argue that the flexibility of virtual environments will play a critical role in many cyber security related aspects. Problems like the assessment of newly devised intrusion detection techniques, the evaluation of skills of cyber defense team members, the evaluation of the disruptive effects caused by the diffusion of new malware, are just few examples of issues that cannot be directly addressed in production systems even though they require realistic operating environments in order to be suitably performed. This paper describes the architecture of SMALLWORLD, a scalable software platform designed to reproduce realistic scenarios achieved by the immersion of real systems into a software defined virtual environment.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130365161","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}
Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847702
An Braeken, A. Touhafi
Cloud computing offers a simple way to provide access to servers, storage, databases and a broad set of application services over the Internet. Its popularity is growing spectacularly. Consequently, there is a need for strong authentication schemes, offering besides privacy also anonymity to the users during these actions. Therefore, this paper presents two userfriendly authentication protocols, able to derive the required security material at user side without the need of a secure channel between user and registration center. The second protocol has the added functionality to guarantee unforgeability and non repudiation of the request. Simple elliptic curve operations, together with hashes and symmetric key encryption algorithms are used. The proposed protocols are two-factor based, requiring password and smartphone, and are very efficient to be executed on the smartphone due to a small amount of computations.
{"title":"Efficient anonymous user authentication on server without secure channel during registration","authors":"An Braeken, A. Touhafi","doi":"10.1109/CLOUDTECH.2016.7847702","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847702","url":null,"abstract":"Cloud computing offers a simple way to provide access to servers, storage, databases and a broad set of application services over the Internet. Its popularity is growing spectacularly. Consequently, there is a need for strong authentication schemes, offering besides privacy also anonymity to the users during these actions. Therefore, this paper presents two userfriendly authentication protocols, able to derive the required security material at user side without the need of a secure channel between user and registration center. The second protocol has the added functionality to guarantee unforgeability and non repudiation of the request. Simple elliptic curve operations, together with hashes and symmetric key encryption algorithms are used. The proposed protocols are two-factor based, requiring password and smartphone, and are very efficient to be executed on the smartphone due to a small amount of computations.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114358232","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}
Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847725
M. H. Heyi, C. Rossi
Despite the growing importance of mobile crowdsourcing applications and cloud computing, little is known about the actual performances of web services deployed within public cloud computing platforms. In order to provide an assessment of the achievable performances in such scenario, we design and implement a back-end general architecture for mobile applications requiring crowdsourcing. We deploy our back-end in the Microsoft Azure cloud computing platform using the PaaS approach, and we evaluate its performance in terms of autoscaling, response time and request rate; while varying the number of instances, the instance type, and the number of concurrent users. Our results shed light on the achievable performances of web services aimed at ingesting crowdsourced data.
{"title":"On the evaluation of cloud web services for crowdsourcing mobile applications","authors":"M. H. Heyi, C. Rossi","doi":"10.1109/CLOUDTECH.2016.7847725","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847725","url":null,"abstract":"Despite the growing importance of mobile crowdsourcing applications and cloud computing, little is known about the actual performances of web services deployed within public cloud computing platforms. In order to provide an assessment of the achievable performances in such scenario, we design and implement a back-end general architecture for mobile applications requiring crowdsourcing. We deploy our back-end in the Microsoft Azure cloud computing platform using the PaaS approach, and we evaluate its performance in terms of autoscaling, response time and request rate; while varying the number of instances, the instance type, and the number of concurrent users. Our results shed light on the achievable performances of web services aimed at ingesting crowdsourced data.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115818174","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}
Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847683
M. Marwan, A. Kartit, H. Ouahmane
Cloud-based medical image is a promising technology. In fact, it provides cost-efficient services and allows collaboration between healthcare ecosystem. Despite its multiple advantages, migrating to this new paradigm arises several challenges: technical, legal and managerial. Recently, the healthcare sector has been interested in adopting this technology to improve the quality of medical care. In this study, we propose a secure framework based on multi-cloud environment. For that, we use a secret share scheme to improve data confidentiality. Moreover, reversible watermarking technique is proposed to verify the integrity of medical image.
{"title":"A secure framework for medical image storage based on multi-cloud","authors":"M. Marwan, A. Kartit, H. Ouahmane","doi":"10.1109/CLOUDTECH.2016.7847683","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847683","url":null,"abstract":"Cloud-based medical image is a promising technology. In fact, it provides cost-efficient services and allows collaboration between healthcare ecosystem. Despite its multiple advantages, migrating to this new paradigm arises several challenges: technical, legal and managerial. Recently, the healthcare sector has been interested in adopting this technology to improve the quality of medical care. In this study, we propose a secure framework based on multi-cloud environment. For that, we use a secret share scheme to improve data confidentiality. Moreover, reversible watermarking technique is proposed to verify the integrity of medical image.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117186051","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}
Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847713
R.S. Shariffdeen, D.T.S.P. Munasinghe, H. S. Bhathiya, U.K.J.U. Bandara, H. Bandara
Elasticity is a key feature of cloud computing where resources are allocated and released according to user demands. Reactive auto scaling, in which the scaling actions take place just after meeting the triggering thresholds, suffers from several issues like risk of under provisioning at peak loads and over provisioning during other times. Proactive scaling solutions, where future resource demand can be forecast and necessary scaling actions enacted beforehand, can overcome these issues. Nevertheless, the effectiveness of such proactive scaling solutions depends on the accuracy of the prediction method(s) adopted. We propose a forecasting technique to enhance the accuracy of workload forecasting in cloud auto-scalers. An ensemble workload prediction mechanism based on time series and machine learning techniques is proposed to make more accurate predictions on drastically different workload patterns. In this work, we initially evaluated several forecasting models for their applicability in forecasting different workload patterns. The proposed ensemble technique is then implemented using three well-known forecasting models and tested for three real-world workloads. Simulation results show that our ensemble method produces significantly lower forecast errors compared to the use of individual models and the prediction technique employed in Apache Stratos, an open source PaaS platform.
{"title":"Adaptive workload prediction for proactive auto scaling in PaaS systems","authors":"R.S. Shariffdeen, D.T.S.P. Munasinghe, H. S. Bhathiya, U.K.J.U. Bandara, H. Bandara","doi":"10.1109/CLOUDTECH.2016.7847713","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847713","url":null,"abstract":"Elasticity is a key feature of cloud computing where resources are allocated and released according to user demands. Reactive auto scaling, in which the scaling actions take place just after meeting the triggering thresholds, suffers from several issues like risk of under provisioning at peak loads and over provisioning during other times. Proactive scaling solutions, where future resource demand can be forecast and necessary scaling actions enacted beforehand, can overcome these issues. Nevertheless, the effectiveness of such proactive scaling solutions depends on the accuracy of the prediction method(s) adopted. We propose a forecasting technique to enhance the accuracy of workload forecasting in cloud auto-scalers. An ensemble workload prediction mechanism based on time series and machine learning techniques is proposed to make more accurate predictions on drastically different workload patterns. In this work, we initially evaluated several forecasting models for their applicability in forecasting different workload patterns. The proposed ensemble technique is then implemented using three well-known forecasting models and tested for three real-world workloads. Simulation results show that our ensemble method produces significantly lower forecast errors compared to the use of individual models and the prediction technique employed in Apache Stratos, an open source PaaS platform.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128030904","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}
Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847691
Imad El Ghoubach, Rachid Benabbou, F. Mrabti
The storage service is one of the most popular services in the cloud. This service allows outsourcing of data storage in service provider servers while having the ability to access it from different devices. Moreover, this service provides the ability to make data-sharing operations with one or more customers, which requires the implementation of a method to maintain data confidentiality while providing granular, scalable and flexible access control. For this reason, several schemes have been proposed. In this paper we propose a scheme, based on cipher-text policy attribute based encryption (CP-ABE), that is able to achieve the desired level of security while having a reduced computation overhead.
{"title":"FP2E: Flexible, effective and privacy preserving cloud data sharing scheme","authors":"Imad El Ghoubach, Rachid Benabbou, F. Mrabti","doi":"10.1109/CLOUDTECH.2016.7847691","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847691","url":null,"abstract":"The storage service is one of the most popular services in the cloud. This service allows outsourcing of data storage in service provider servers while having the ability to access it from different devices. Moreover, this service provides the ability to make data-sharing operations with one or more customers, which requires the implementation of a method to maintain data confidentiality while providing granular, scalable and flexible access control. For this reason, several schemes have been proposed. In this paper we propose a scheme, based on cipher-text policy attribute based encryption (CP-ABE), that is able to achieve the desired level of security while having a reduced computation overhead.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131869559","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}
Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847688
B. Zerhari
Noise points, or class noise, detection and elimination became increasingly important to handle large datasets. In fact, eliminating noise in this environment helps reduce computing costs, especially when using clustering algorithms. Nowadays, large varieties of clustering algorithms exist and produce good results. However, they often assume that the input data are free or have very low level of noise, which is rarely the case in real Big Data context. In this paper, we present a noise detection and elimination approach for large datasets. This approach relies on four important steps: divide data into subsets, extract the best rules, apply different classifiers to the subsets, and finally combine the classifiers results.
{"title":"Class noise elimination approach for large datasets based on a combination of classifiers","authors":"B. Zerhari","doi":"10.1109/CLOUDTECH.2016.7847688","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847688","url":null,"abstract":"Noise points, or class noise, detection and elimination became increasingly important to handle large datasets. In fact, eliminating noise in this environment helps reduce computing costs, especially when using clustering algorithms. Nowadays, large varieties of clustering algorithms exist and produce good results. However, they often assume that the input data are free or have very low level of noise, which is rarely the case in real Big Data context. In this paper, we present a noise detection and elimination approach for large datasets. This approach relies on four important steps: divide data into subsets, extract the best rules, apply different classifiers to the subsets, and finally combine the classifiers results.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128709252","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}
Pub Date : 2016-05-01DOI: 10.1109/CLOUDTECH.2016.7847709
Yassir Samadi, M. Zbakh, C. Tadonki
Big Data is currently a hot topic for companies and scientists around the world, due to the emergence of new technologies, devices and communication means like social network sites, which led to a noticeable increase of the amount of data produced every year, even every day. In addition, traditional algorithms and technologies are inefficient to process, analyze and store this vast amount of data. So, to solve this problem, Big Data frameworks are needed. In this paper, we present and discuss a performance comparison between two popular Big Data frameworks. Hadoop and Spark, which are used to efficiently process vast amount of data in parallel and distributed mode on a large clusters. Hibench benchmark suite is used to compare the performance of these two frameworks based on the criteria as execution time, throughput and speedup. Our experimental results show that Spark is more efficient than Hadoop to deal with large amount of data. However, spark requires higher memory allocation, since it loads processes into memory and keeps them in caches for a while, just like standard databases. So the choice depends on performance level and memory constraints.
{"title":"Comparative study between Hadoop and Spark based on Hibench benchmarks","authors":"Yassir Samadi, M. Zbakh, C. Tadonki","doi":"10.1109/CLOUDTECH.2016.7847709","DOIUrl":"https://doi.org/10.1109/CLOUDTECH.2016.7847709","url":null,"abstract":"Big Data is currently a hot topic for companies and scientists around the world, due to the emergence of new technologies, devices and communication means like social network sites, which led to a noticeable increase of the amount of data produced every year, even every day. In addition, traditional algorithms and technologies are inefficient to process, analyze and store this vast amount of data. So, to solve this problem, Big Data frameworks are needed. In this paper, we present and discuss a performance comparison between two popular Big Data frameworks. Hadoop and Spark, which are used to efficiently process vast amount of data in parallel and distributed mode on a large clusters. Hibench benchmark suite is used to compare the performance of these two frameworks based on the criteria as execution time, throughput and speedup. Our experimental results show that Spark is more efficient than Hadoop to deal with large amount of data. However, spark requires higher memory allocation, since it loads processes into memory and keeps them in caches for a while, just like standard databases. So the choice depends on performance level and memory constraints.","PeriodicalId":133495,"journal":{"name":"2016 2nd International Conference on Cloud Computing Technologies and Applications (CloudTech)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123440822","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}
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