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i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 10 Issue 2

Most Cited

Volume 3 Issue 3 May - July 2016

Research Paper

Resolving Multitenancy Issues in Cloud Computing Service Models Using Software Defined Networks

Chinthagunta Mukundha*

*Associate Professor, Department of Information Technology, Sreenidhi Institute of Science and Technology, Ghatakesar, Hyderabad, Telangana, India.

Mukundha, C. (2016). Resolving Multitenancy Issues in Cloud Computing Service Models Using Software Defined Networks. i-manager’s Journal on Cloud Computing, 3(3), 1-8. https://doi.org/10.26634/jcc.3.3.8295

Abstract

Now-a-days Cloud Computing has become more popular in all Information Technology areas. Cloud computing provides a number of services like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). All these cloud computing models share common resources with different customers to provide services. To provide services to the customers, cloud computing uses a technique called Multitenancy. When cloud uses Multitenancy to provide services, there are so many data and security problems. Cloud security is becoming a major issue in adopting the cloud, where security is considered as one of the major critical concerns for the large customers of cloud. Multitenancy is a technique of sharing resources of cloud commonly across different customers. In SaaS applications, same database is shared by different users. If a common database is shared among different customers, there are chances for so many data related security issues like data loss or leakage, Account or session hijacking, etc., to occur. In this paper, the author has proposed solutions for the problems that occur in cloud computing models with Multitenancy by implementing secure key mechanism using Software Defined Networks (SDN) in the cloud computing service model.

References

[1]. Cloud Security Alliance, (2010). Top Threats to Cloud Computing V1.0.

[2]. Jon Brodkin, (2008). Gartner: Seven Cloud- Computing Security Risks. Infoword.

[3]. Dimitrios Zissis, and Dimitrios Lekkas, (2011). “Addressing cloud computing security issues”. Future Generation Computer Systems, Vol. 28, No. 3, pp. 583- 592.

[4]. S. Subashini, and V. Kavitha, (2011). “A Survey on security issues in service delivery models of cloud computing”. Journal of Network and Computer Applications, Vol. 34, No. 1, pp. 1-11.

[5]. Wayne A. Jansen, (2011). “Cloud Hooks: Security and privacy issues in cloud computing”. Proceedings of the 44^th Hawaii International Conference on System Sciences, pp. 1-10.

[6]. David Teneyuca, (2011). “Internet cloud security: The illusion of inclusion”. Information Security Technical Report, Vol. 16, No. 3-4, pp. 102-107.

[7]. Afkham Azeez, Srinath Perera, Dimuthu Gamage, Ruwan Linton, Prabath Siriwardana, Dimuthu Leelaratne, Sanjiva Weerawarana, and Paul Fremantle, (2010). “Multi-Tenant SOA middleware for cloud computing”. IEEE 3^rd International Conference on Cloud Computing, pp. 458-465.

[8]. Verizon RISK Team, (2012). Data Breach Investigations Report (DBIR).

[9]. Prasad Saripalli, and Ben Walters, (2010). “QUIRC: A quantitative impactand risk assessment framework for cloud security”. IEEE 3^rd International Conference on Cloud Computing.

[10]. Hagai Bar-El, (n.d.). Introduction to Side Channel Attacks. Discretix technologies Ltd.

[11]. S. Pearson and A. Benameur, (2010). “Privacy, Security and Trust Issues Arising from Cloud Computing”. IEEE Second International Conference on Cloud Computing Technology and Science, Vol. 8, No. 6, pp. 692–702.

[12]. Ruoyu Wu, Gail-Joon Ahn, Hongxin Hu, and Mukesh Singhal, (2010). “Information flow control in cloud computing”. IEEE Transactions on Network and Service management, Vol. 11, No. 1, pp. 76-89.

[13]. D. Milojicic, (2000). “Security and Privacy”. IEEE Concurrency, Vol. 8, No. 2, pp. 24–21.

[14]. Augusto Ciuffoletti, (2010). “Monitoring a virtual network infrastructure: An Iaas Perspective”. ACM SIGCOMM Computer communication Review, Vol. 40, No. 5, pp. 47-52.

[15]. Z. Chen and J. Yoon, (2010). “IT Auditing to Assure a Secure Cloud Computing”. 6^th World Congress on Services, pp. 252– 259.

[16]. S. Bleikertz, M. Schunter, C.W. Probst, and K. Eriksson, (2010). “Security Audits of Multi-tier Virtual Infrastructures in Public Infrastructure Clouds Categories and Subject Descriptors”. CCSW’10, pp. 92–102.

[17]. R. Chakraborty, S. Ramireddy, T.S. Raghu, and H.R. Rao, (2010). “The Information Assurance Practices of Cloud Computing”. IT Professional, Vol. 12, No. 4, pp. 29–37.

[18]. R. Grandl, Y. Chen, J. Khalid, S. Yang, A. Anand, T. Benson, and A. Akella, (2013). “Harmony: Coordinating network, compute, and storage in software-defined clouds”. In Proceedings of the 4^th Annual Symposium on Cloud Computing.

[19]. S.A. Baset, L. Wang, B.C. Tak, C. Pham, and C. Tang, (2014). “Toward achieving operational excellence in a cloud”. IBM Journal of Research and Development, Vol. 58, No. 2/3.

[20]. H. Alaqrabi, Lu Liu, Jie Xu, Richard Hill, Nick Antono poulos,and ongzhao Zhan,(2012) . “Investigation of IT security and compliance challenges in security-as-a-Service for cloud computing”. 15^th IEEE International Symposium on Object/Component/ Service-Oriented Real-Time Distributed Computing Workshops (ISORCW).

[21]. Eric Keller, Jakub Szefer, Jennifer Rexford, and Ruby B. Lee, (2010). “NoHype: virtualized cloud infrastructure without the virtualization”. The 27^th Annual International Symposium on Computer Architecture.

[22]. Francisco Rocha, Thomas Gross, and Aad van Moorsel, (2012). “Defense in-depth Against Malicious Insiders in the Cloud”. IEEE International Conference on Cloud Engineering.

[23]. Peng Li, Debin Gao, and Michael K. Reiter, (2012). “Mitigating Access-Driven Timing Channels in Clouds using StopWatch”. The 42^nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks.

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Research Paper

Authenticating users with multiple levels of validations in a secure Cloud Computing Environment

Pranayanath Reddy Anantula* , G. Manoj Someswar**

* Assistant Professor, Department of Computer Science and Engineering, Alliance University, Bangalore, Karnataka, India.

** Director General & Scientist-G, Global Research Academy, Hyderabad, Telangana, India.

Anantula, P. R., and Someswar, G. M. (2016). Resolving Authenticating users with multiple levels of validations in a secure Cloud Computing Environment. i-manager’s Journal on Cloud Computing, 3(3), 9-17. https://doi.org/10.26634/jcc.3.3.8296

Abstract

In the current digital technology world, all the applications are moving into cloud environment. The biggest challenge is to maintain the security and privacy of the application in cloud. One of the basic security features that normally applied in any application is Authentication of Users using valid user id and password. Authenticating users only on these two parameters will not assure that the user who had entered these details is an actual user. Recently most of the applications are using two factor authentications (OTP's) over the user id and password. In cloud computing environment we can use different authentication functionalities depending upon the customer requirements. In this paper we will explore eight authentication functionalities and discuss their pros and con's in implementing them in the applications. For strong level of security, two or more functionalities can be combined and implemented in the application to validate the credentials of the user.

References

[1]. D. Jayalatchumy, P. Ramkumar, and D. Kadhirvelu, (2010). “Preserving Privacy through Data Control in a Cloud Computing Architecture using Discretion Algorithm”. Third International Conference on Emerging Trends in Engineering and Technology, IEEE, DOI 10.1109/ICETET.2010.103.

[2]. Wang, B., Baochun, and Wang, H.L. (2012). “Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud”. IEEE Fifth International Conference on Cloud Computing, DOI 10.1109/CLOUD.2012.46.

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[8]. UserGuide: How to be Successful with Salesforce. Winter '16 Edition.

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Research Paper

Subspace Clustering on High Dimensional Data

Bhargav* , P. Venkateswarlu Reddy**

* PG Scholar, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

** Assistant Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

Bhargav, M., and Reddy, P. V. (2016). Subspace Clustering On High Dimensional Data. i-manager’s Journal on Cloud Computing, 3(3), 18-26. https://doi.org/10.26634/jcc.3.3.8297

Abstract

Due to the increase of large amount of real world, it is a difficult task for the organizations, companies, etc., to extract relevant data from large amounts of necessary and unnecessary data. Many researches will be going on from the few decades onwards. In datamining there is a concept called clustering which will be used for the smaller datasets, where it effectively makes relevant data into clusters. But the problem will arise on the larger datasets, where it will face a complexity for grouping relevant data into cluster. In this paper, analysis have been referred from many of the algorithms like subscale algorithm for finding the dense region from the dataset and DBscan algorithm for making a cluster as a result it takes dataset as an input and scans a complete dataset. The problem occurs on the time complexity and performance and also, it will follow a sequential flow of database scan. So, it takes time for relevant data values as a cluster in final result. In this analysis, it will allow the complete dataset scans at a time, processing data in parallel manner. So, the resultant data is in effective manner in a lesser time. For the improvement of previous algorithm a Map Red based DBscan for reducing time complexity and performance improvement has been used.

References

[1]. Elhamifar E., and Vidal R., (2013). “Sparse subspace clustering: Algorithm, theory, and applications”. IEEE Trans. Pattern Anal Mach Intell., Vol. 35, No. 11, pp. 2765- 2781.

[2]. Günnemann S., Boden B., and Seidl T., (2012). “Finding density-based subspace clusters in graphs with feature vectors”. In: Data Mining and Knowledge Discovery, Springer, US, Vol. 25. pp. 243-269.

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[4]. Kriegel HP., Kröger P., Zimek A., and Oger PKR, (2009). “Clustering high-dimensional data: A survey on subspace clustering, pattern based clustering, and correlation clustering”. ACM Trans Knowl Discov Data, Vol. 3, No. 1, pp. 1–58.

[5]. Yoon S., Nardini C., Benini L., and De Michelin G., (2005). “Discovering coherent biclusters from gene expression data using zero-suppressed binary decision diagrams”. IEEE/ACM Trans Comput BiolBioinforma, Vol. 2, No. 4, pp. 33.

[6]. Xu R., and Wunsch D., (2005). “Survey of clustering algorithms”. Neural Netw IEEE Trans., Vol. 16, No. 3, pp. 645-678.

[7]. Jiang D., Tang C., and Zhang A., (2004). “Cluster analysis for gene expression data: A survey”. IEEE Trans Knowl Data Eng, Vol. 16, No. 11, pp. 1370–1386.

[8]. Parsons L., Haque E., and Liu H., (2004). “Subspace clustering for highdimensional data: A review”. ACM SIGKDD Explor Newspp, Vol. 6, No. 1, pp. 9105.

[9]. Steinbach M., Ertoz L., and Kumar V., (2004). “The challenges of clustering high dimensional data”. In: New Directions in Statistical Physics, Springer, Berlin Heidelberg, pp.273–309.

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Research Paper

Page Navigation Prediction Based on Longest Common Subsequence and Apriori Algorithm

Megha Mishra* , Harsha Dubey, Vishnu Kumar Mishra*

* Senior Assistant Professor, Department of Computer Science & Engineering, Chhattisgarh Swami Vivekanand Technical University, Chhattisgarh, India.

Research Scholar, Department of Computer Science & Engineering, Chhattisgarh Swami Vivekanand Technical University, Chhattisgarh, India.

* Associate Professor, Bharti College of Engineering of Technology, Durg, India.

Mishra, M., Dubey, H., and Mishra, V. K. (2016). Page Navigation Prediction Based On Longest Common Subsequence And Apriori Algorithm. i-manager’s Journal on Cloud Computing, 3(3), 27-32. https://doi.org/10.26634/cc.3.3.8298

Abstract

World Wide Web is a huge repository of web pages and links. It provides abudance of information for the Internet users. Web mining is the use of Data mining frameworks to actually discover and concentrate data from web archives and administrations. Web mining is three sorts: Web use Mining, Web content Mining, and Web structure Mining. Web utilization mining is the process of finding information from the cooperation created by the clients in the types of access logs, program logs, intermediary server logs, client session information, treats. A huge amount of user request data is generated in a web log. Predicting user's requests based on previously visited pages is important for the web page recommendation, reduction of latency, and online advertising. The web server log document is naturally made and kept up by a server comprising of a rundown of exercises it performed. The proposed framework is intended for website page forecast in suggestion framework and also it is useful for the investigation of web mining calculation to get incessant consecutive access design from the web log document on the web server. After cleaning, and applying the longest common subsequence and Apriori algorithm, the outcomes of the effective calculation of web log access are inaccurate.

References

[1]. Priyanka S. Panchal, and Urmi D. Agravat, (2013). “Hybrid Technique for User's Web Page Access Prediction based on Markov Model”. IEEE, 4^th ICCCNT 2013, Tiruchengode, India.

[2]. Smriti Pandya, and Rajesh Nigam, (2015). “Review Paper on Web Page Prediction Using Data Mining”. IISTE, Vol.6.

[3]. Prasad J. Koyande, and Kavita P. Shirsat, (2015). “Comparison of Different Navigation Prediction Techniques”. IJCSIT, Vol. 6 (2).

[4]. Shaikh Sakina Banu, and Meera Narvekar, (2015). “Predicting User's Web Navigation Behaviour using Hybrid Approach”. International Conference on Advanced Technologies and Application.

[5]. Kaushal Kishore Sharma and Kiran Agrawal, (2014). “A Hybrid Approach for Predicting User's Future Request”. International Conference on Communication System and Network Technologies.

[6]. Arshi Shamsi, Rahul Nayak, Pankaj Pratap Singh, and Mahesh Kumar Tiwari, (2012). “Web Usage Mining by Data Preprocessing”. IJCST, Vol. 3, No. 1.

[7]. Poornalatha G, and Prakash S. Raghavendra, (2012). “Web Page Prediction by Clustering and Integrate Distance Measures”. IEEE/ ACM Trans. Syst., Man, Cybern. A syst., Humans, Vol. 44, No. 2.

[8]. Thanakorn Pamutha, Siriporn Chimphlee, Chom Kimpan, and Parinya Sanguansat, (2012). “Data Preprocessing on Web Server Log Files for Mining Users Access Patterns”. International Journal of Research and Reviews in Wireless Communication (IJRRWC), Vol. 2, No. 2.

[9]. C.P. Sumathi, R. Padmaja Valli, and T. Santhanam, (2011). “An Overview of Preprocessing of Web Log Files for Web Usage Mining”. Journal of Theoretical and Applied Information Technology, Vol. 34, No. 2, IC.P.

[10]. Deepti Razdan, (2011). “The Next Page Access Prediction Using Markov Model”. IJECCT, Vol. 1, No. 1.

[11]. L.K. Joshila Gracel, V. Maheswari, and Dhinaharan Nagamalai, (2011). “Analysis of Web Logs and Web User in Web Mining”. International Journal of Network Security & its Applications, Vol. 3, No. 1.

[12]. Navin Kumar Tyagi, A.K. Solanki, and Manoj Wadhwa, (2010). “Analysis of Server Log By Web Usage Mining for Website Improvement”. IJCSI International Journal of Computer Science, Vol. 7, No. 4.

[13]. Priyanka Makkar, Payal Gulati, and A.K. Sharma, (2010). “A Novel Approach for Predicting User Behaviour for Improving Web Performance”. IJCSE International Journal of Computer Science and Engineering, Vol. 2, No. 4.

[14]. Srivasta, J., Cooley, R., Deshpande, M., and Tan P.N., (2000). “Web Usage Mining: Discovery and Application of Web Usage Pattern From Web Data”. Department of Computer Science and Engineering, University of Minnesota.

[15]. Kosala, R., and Blockeel, H., (2000). “Web Mining Research: A Survey”. ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) Explorations, pp. 1-10.

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Review Paper

A Review on Disruptive Technology – “Cloud Computing” -Roadmap to Models and Security Threats and Causes

Sabibullah Mohamed Hanifa*

*Associate Professor & Dean, PG & Research Department of Computer Science, Sudharsan College of Arts & Science, Tamilnadu, India.

Hanifa, S. M. (2016). A Review on Disruptive Technology – “Cloud Computing” -Roadmap to Models and Security Threats and Causes. i-manager’s Journal on Cloud Computing, 3(3), 33-39. https://doi.org/10.26634/jcc.3.3.8299

Abstract

Cloud Computing (CC) is accomplished on the top of Gartner's list of the ten most disruptive technologies of the next years. It has been measured as a significant step towards achieving the long-held dream of envisioning computing as a utility. It provides a great chance to supply storage, processing, and visualization resources for sensing-based healthcare data also. Flexibility, scalability, computing and application resources, optimal utilization of infrastructure and reduced costs are the major cloud-based services. CC comes with abundant possibilities and challenges concurrently of which security / threats is most well-thought out vital barrier for cloud computing path to success. When putting and transmitting data using public networks exposed to the world, cyber attacks in any form are anticipated in CC. Hence, cloud service users need to understand the risk of data breaches and adoption of service delivery model along with cloud deployment model in the cloud environment. This paper covers the roadmap on this machinery in detail so that “robust security application models” can be created / deployed easily with possible uncovered breaches.

References

[1]. Gartner, (2008). “Gartner top ten disruptive technologies for 2008 to 2012”. Emerging Trends and Technologies Roadshow. Technical Report.

[2]. J. Geelan, (2008). “Twenty one experts define cloud computing virtualization”. Containers Expo Journal. Retrieved from http://virtualization.sys-con. com /node/612375

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i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 10 Issue 2

Most Read

Most Cited

Volume 3 Issue 3 May - July 2016

Resolving Multitenancy Issues in Cloud Computing Service Models Using Software Defined Networks

Chinthagunta Mukundha*

*Associate Professor, Department of Information Technology, Sreenidhi Institute of Science and Technology, Ghatakesar, Hyderabad, Telangana, India.

Mukundha, C. (2016). Resolving Multitenancy Issues in Cloud Computing Service Models Using Software Defined Networks. i-manager’s Journal on Cloud Computing, 3(3), 1-8. https://doi.org/10.26634/jcc.3.3.8295

Abstract

References

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Authenticating users with multiple levels of validations in a secure Cloud Computing Environment

Pranayanath Reddy Anantula* , G. Manoj Someswar**

* Assistant Professor, Department of Computer Science and Engineering, Alliance University, Bangalore, Karnataka, India. ** Director General & Scientist-G, Global Research Academy, Hyderabad, Telangana, India.

Anantula, P. R., and Someswar, G. M. (2016). Resolving Authenticating users with multiple levels of validations in a secure Cloud Computing Environment. i-manager’s Journal on Cloud Computing, 3(3), 9-17. https://doi.org/10.26634/jcc.3.3.8296

Abstract

References

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Subspace Clustering on High Dimensional Data

Bhargav* , P. Venkateswarlu Reddy**

* PG Scholar, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India. ** Assistant Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

Bhargav, M., and Reddy, P. V. (2016). Subspace Clustering On High Dimensional Data. i-manager’s Journal on Cloud Computing, 3(3), 18-26. https://doi.org/10.26634/jcc.3.3.8297

Abstract

References

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Page Navigation Prediction Based on Longest Common Subsequence and Apriori Algorithm

Megha Mishra* , Harsha Dubey**, Vishnu Kumar Mishra***

Mishra, M., Dubey, H., and Mishra, V. K. (2016). Page Navigation Prediction Based On Longest Common Subsequence And Apriori Algorithm. i-manager’s Journal on Cloud Computing, 3(3), 27-32. https://doi.org/10.26634/cc.3.3.8298

Abstract

References

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A Review on Disruptive Technology – “Cloud Computing” -Roadmap to Models and Security Threats and Causes

Sabibullah Mohamed Hanifa*

*Associate Professor & Dean, PG & Research Department of Computer Science, Sudharsan College of Arts & Science, Tamilnadu, India.

Hanifa, S. M. (2016). A Review on Disruptive Technology – “Cloud Computing” -Roadmap to Models and Security Threats and Causes. i-manager’s Journal on Cloud Computing, 3(3), 33-39. https://doi.org/10.26634/jcc.3.3.8299

Abstract

References

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* Assistant Professor, Department of Computer Science and Engineering, Alliance University, Bangalore, Karnataka, India.

** Director General & Scientist-G, Global Research Academy, Hyderabad, Telangana, India.

* PG Scholar, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

** Assistant Professor, Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, India.

Megha Mishra* , Harsha Dubey, Vishnu Kumar Mishra*