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

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An OptiAssign-PSO Based Optimisation for Multi-Objective Multi-Level Multi-Task Scheduling in Cloud Computing Environment
Advanced Analytics for Disease Tracking & Remote Intravenous Injection Monitoring
DevKraft - Fueling Collaboration in Coding Challenges
Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions
Blockchain Healthcare Management using Patients

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Volume 1 Issue 1 November - January 2014

Article

Cloud Security Requirements

Poonam Rawat* , Neha Rawat, Shikha singh*, Awantika****

*-**** Students, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar.

Rawat, P., Rawat ,N., Singh, S., and Awantika (2014). Cloud Security Requirements. i-manager’s Journal on Cloud Computing, 1(1), 1-5. https://doi.org/10.26634/jcc.1.1.2797

Abstract

Cloud computing brings new opportunity and challenges for IT industry. Basically, Cloud computing provides you to access your information from anywhere at any time. So, Cloud computing security is a major concern for cloud service providers, developers and also for users who are using this technology everyday. And ensuring cloud security has become a burning topic in IT industry and research era. The goal of this paper is to provide all the cloud security requirements which should be properly understood for giving cloud its full potential. Taking those requirements, cloud service providers will be able to deliver an efficient and secure service on cloud to individual customers and enterprise. This will encourage the adoption of cloud computing not only on small enterprises, but all over the world on large scales also.

References

[1]. Dimitrios Zissis, Dimitrios Lekkas (2012). “Addressing cloud computing security issues”, Future Generation Computer Systems. pp.28 583–592.

[2]. S. Subashini and V. Kavitha (2011). “A survey on security issues in service delivery models of cloud computing”, Journal of Network and Computer Applications. pp.34, 1–11.

[3]. Su Qinggang, Wang Fu and Hang Qiangwei (2012). “Study of Cloud Computing Security Service Model”, IEEE 978-1-4577-1964-6/12,

[4]. Mutum Zico Meetei and Anita Goel (2012). “Security Issues in Cloud Computing”, 5th International Conference on BioMedical Engineering and Informatics. BMEI .

[5]. Patrick Höner (2013). ”Cloud Computing Security Requirements and Solutions: a Systematic Literature Review”,19thTwente Student Conference on IT, June 24th.

[6]. Ramgovind S, Eloff MM, Smith E (2010). ”The Management of Security in Cloud Computing”, IEEE. pp.978-1-4244-5495-2/10 .

[7]. Peter Mell, and Tim Grance (2010). “The NIST Definition of Cloud Computing , "Retrieve from http://www.wheresmyserver.co.nz/storage/media/faqfiles/ clouddef-v15.pdf, Accessed April .

[8]. Cloud Security Alliance (2009). “Security guidance for critical areas of focus in cloud computing” ,V2.1.

[9]. Michel Miller. “Cloud computing web-based applications that change the way you work and collaborate online”, Indianapolis, Indiana

[10]. A. Priyadharshini (2013). “A Survey on security issues and countermeasures in cloud computing storage and a tour towards multi-cloud”. International Journal of Research in Engineering and Technology (IJRET). Vol. 1, Issue 2, July . 4

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Research Paper

Throughput Variant Component Ranking In Dynamic FT Cloud Framework

Sudheshna tupadha* , Shoba Bindu C**

* Department of CSE, JNTUA College of Engineering, Ananthapuram, India

** Associate Professor & Head, Department of CSE, JNTUA College of Engineering, Ananthapuram, India

Sudheshna, T., and Bindu, C. S. (2014). Throughput Variant Component Ranking In Dynamic FT Cloud Framework. i-manager’s Journal on Cloud Computing, 1(1), 6-12. https://doi.org/10.26634/jcc.1.1.2798

Abstract

FTCloud is an emerging cloud paradigm that orchestrates multiple cloud technologies and is becoming the main stream aspect of providing service. As software, Fault Tolerance (FT) mechanisms mask failures earlier to improve reliability. To address this challenge, Zibin Zheng proposed a component ranking framework with fault tolerance named FTCloud to tolerate failures in software. In FTCloud more characteristic factors like throughput and dynamic faulttolerance mechanisms are not implemented. To ensure reliability ' Dynamic FTCloud ' framework mainly concentrates on throughput with random graph model in FTCloud1 while employing response time for services. The FTCloud2 focuses on failure probability of components as extension to the FTCloud. In this paper, dynamic optimal fault-tolerance strategy is implemented in the framework along with the previous algorithm of design diversity techniques .The prospecting results show that tolerating faults of significant components are having enormous improvement with reliability.

References

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[13]. Z. Zheng and M.R. Lyu (2008). “A Distributed Replication Strategy Evaluation and Selection Framework for Fault Tolerant Web Services,” Proc. Sixth Int'l Conf. Web Services, pp. 145-152.

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[17]. Zibin Zheng, Xinmiao Wu, Yilei Zhang, Michael R. Lyu, Jianmin Wang (2013). "QoS Ranking Prediction for Cloud Services," IEEE Transactions on Parallel and Distributed Systems, Vol. 24, No. 6, pp. 1213-1222, June 2013.

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[20]. Avizienis, A (1997). “On the Implementation of NVersion Programming for Software Fault- Tolerance During Execution,” COMPSAC '77, Chicago, IL, pp. 149–155.

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[25]. Zibing Zheng and M.R.Lyu (2011). ”Component Ranking for Fault-Tolerant Cloud Applications”, IEEE Transaction on Service Computing. Vol.5(4).

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Research Paper

Delegated Access Control In Public Clouds Using Two Layer Encryption

Malathi.P*

PG Scholar, Muthayammal College of Engineering, Rasipuram, Nammakkal (DT), Tamilnadu, India.

Malathi, P. (2014). Delegated Access Control in Public Clouds Using Two Layer Encryption. i-manager’s Journal on Cloud Computing, 1(1), 13-18. https://doi.org/10.26634/jcc.1.1.2799

Abstract

A cloud is a collection of terminals and servers that are publicly accessible via the internet. One of the primary uses of cloud computing is data storage. In cloud computing, data is stored in encrypted form to ensure confidentiality. Here the user performs verification during data storage process. So the data owner requires a Third Party Auditor [TPA] for auditing. TPA audits the files stored in the cloud. During the audit process, TPA gets the keys from the data owner and views the data for the auditing. The major drawback here is TPA can modify the owner data. So, the proposed system implements the encrypting data and notification method, so that the TPA views the data in encrypted form. For encrypted data auditing, dynamic Provable Data Possession (PDP) is used. But if a TPA tries to decrypt the owner data, then Provable Of Retrievable (POR) sends the notification to the data owner. Until the owner verifies the notification, the modification will not be committed to the database.

References

[1]. Alina Oprea, Michael K. Reiter,Ke Yang (2005). “Space-efficient block storage integrity”, In Proc of NDSS on 2005.

[2]. C. C. Erway, A. Kupc¸ U, C. Papamanthou, and R. Tamassia (2011). “An improved dynamic provable data possession model” Cloud Computing and Intelligence Systems (CCIS), 2011 IEEE International Conference on, Sept. 2011.

[3]. Cong Wang, Qian Wang, and Kui Ren (2009). “Ensuring data storage security in Cloud Computing” Quality of Service IWQoS 17th International Workshop on, July 2009.

[4]. G. Ateniese, R. D. Pietro, L. V. Mancini, and G. Tsudik (2013). “Scalable and efficient provable data possession” Secure Comm Security and Privacy in Communication Networks, June 2013, Volume 18, Issue 3, pp. 265-271.

[5]. Hovav Shacham, Brent Waters (2013). “Compact proofs of retrievability”, Journal of Cryptology, July 2013, Volume 26, Issue 3, pp 442-483.

[6]. Junkil Park,Jin-Young choi (2007). “Formal Security Policy Model For Commaon Criteria Evaluation” Advanced Communication Technology. The 9^th International Conference (Volume,1), Feb 2007.

[7]. Mehul A. Shah Ram Swaminathan Mary Baker (2008). 'Privacy-preserving audit and extraction of digital contents', Hewlett-Packard, on Apr 2008.

[8]. Mohamed Nabeel, Ellisa Bertino (2013). “Privacy preserving delegated access control in public clouds” IEEE International Conference, 2013.

[9]. Qian Wang, Kui Ren ,Wenjing Lou ,Yanchao Zhang (2009). “Dependable and Secure Sensor Data Storage with Dynamic Integrity Assurance” INFOCOM 2009, IEEE, April 2009.

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Research Paper

Online Anomaly Based Intrusion Detection System Using Machine Learning

D.P. Gaikwad* , R.C. Thool**

* Assistant Professor, Department of Computer Engineering, All India Shri Shivaji Memorial Society’s College of Engineering, Pune.

** Professor, Head of Department of Information technology, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded

Gaikwad, D. P., and Thool, R. C. (2014). Online Anomaly Based Intrusion Detection System Using Machine Learning. i-manager’s Journal on Cloud Computing, 1(1), 19-25. https://doi.org/10.26634/jcc.1.1.2800

Abstract

As the cost of information processing and Internet accessibility falls, organizations are becoming gradually defenceless to potential cyber threats such as network intrusions. So, there exists a need to run secure and safe transactions through the use of Intrusion Detection Systems, authentication, firewall and other hardware and software solutions. The existing Intrusion Detection system abilities to be adapted are very limited. This makes them ineffective for new or unknown attacks detection or to be adapted to an evolutionary environment. Machine learning approaches offer a potential solution to adaptation and correctness problems in Intrusion detection.Some Intrusion Detection systems does not deal with real time high speed networks. The high false positive rate is another issue with existing intrusion detection systems. In this paper, we present the machine learning approach for Intrusion Detection system which helps to reduce the false positive rates and increase the classification accuracy. We are going to train our system using the Real time data set using Naïve Bayes machine learning algorithm. The role of our system is to attempt to trap an adversary's attendance on a compromised network. Our System notices vulnerable packets that are trying to come into the network. We capture live packets and extract only the relevant header features.This improves the accuracy of the proposed system.Finally, using Naïve Based off-line trainer, we were able to achieve 90.2233 percent accuracy using Cross Validation of 10-fold and 76.6812 percent using supplied test dataset while maintaining 0.102 false positive rates.

References

[1]. Christine Dartigue, Hyun Ik Jang and Wensum Zeng (2009). “A New Data-Mining Based Approach for Network Intrusion Detection”, In proceeding of IEEE Seventh Annual Communication Networks and Services Research Conference.

[2]. Sandhya Peddabachigari, Ajith Abraham and Johnson Thomas (2008). “Intrusion Detection Systems Using Decision Trees and Support Vector Machines“ ,Department of Computer Science, Oklahoma State University, USA .

[3]. G.Prashanth, V.Prashanth, P.Jayashree and N.Srinivasan (2008). “Using Random Forests for Networkbased Anomaly detection at Active routers”, In proceeding of IEEE-International Conference on Signal processing, Communications and Networking. Madras Institute of Technology, Anna University Chennai India, pp93-96.Jan.

[4]. MehdiMoradi and Mohammad ZULKERNINE. “A Neural Network Based System for Intrusion Detection and Classification of Attacks”, pp.148-04.

[5]. Amira Sayed A. Aziz Mostafa A. Salama,Aboul ella Hassanien and Sanaa El-Ola Hanafi (2012). “Artificial Immune System Inspired Intrusion Detection System Using Genetic Algorithm”. In Informatics 36 (2012), pp. 347–357.

[6]. Jonathan Palmer (2011). “Naive Bayes Classification for Intrusion Detection Using Live Packet Capture”. In Data Mining in Bioinformatics, Spring.

[7]. Salem Benferhat, Abdelhamid Boudjelida and Habiba Drias (2009). “An Intrusion Detection Approach Based on Tree Augmented Naive Bayes and Expert Knowledge”.

[8]. Amjad Hussain Bhat, Sabyasachi Patra and Dr. Debasish Jena (2013). “,Machine Learning Approach for Intrusion Detection on CloudVirtual Machines” In IEEE Conference.

[9]. Upendra (2013). “An Efficient Feature Reduction Comparison of Machine Learning Algorithms for Intrusion Detection System”. International Journal of Emerging Trends & Technology in Computer Science on ,Volume 2(1), January.

[10]. Mohan Banerjee and Roopali Soni(2013). “Design and Implementation of Network Intrusion Detection System by using K-means clustering and Naïve Bayes”. International Journal of Science, Engineering and Technology Research, Volume 2, Issue 3, March 2013.

[11]. Neethu B (2009). “Classification of Intrusion Detection Dataset usingmachine learning Approaches“, International Journal of Electronics and Computer Science Engineering. ISSN 2277-1956/V1N3-1044-1051.

[12]. Dewan Md. Farid1, Nouria Harbi1 and Mohammad Zahidur Rahman (2010). “Combining Naïve Bayes and Decision Tree For Adaptive Intrusion Detection”. International Journal of Network Security & Its Applications (IJNSA), Volume 2, Number 2, April 2010.

[13]. Mrutyunjaya Panda and Manas Ranjan Patra (2007).“Network Intrusion Detection Naïve Bayes“ International Journal of Computer Science and Network Security on VOL.7 No.12, December 2007258,Manuscript received December 5, 2007,Manuscript revised December 20, 2007

[14]. D.P.Gaikwad and Dr.R.C.Thool (2010). “Architecture Taxonomy and Product of IDS”, in proceeding of International Conference on Computer Applications, Computer Application-II, and doi: 10.3850/978-981-08- 7304-2_0382.

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Review Paper

Techniques To Ensure Data Integrity In Cloud Survey

C.Sasikala* , Shoba Bindu C**

* Lecturer, Department of CSE, JNTUA College of Engineering, Anantapuramu, India.

** Associate Professor & Head, Department of CSE, JNTUA College of Engineering, Anantapuramu, India.

Sasikala, C., and Bindu, C. S. (2014). Techniques to Ensure Data Integrity in Cloud Survey. i-manager’s Journal on Cloud Computing, 1(1), 26-32. https://doi.org/10.26634/jcc.1.1.2802

Abstract

Cloud Storage also known as data storage as a service, is one of the most popular cloud computing services. It allows the clients to release their burden of storing and maintaining the data locally by storing it over the cloud. Cloud storage moves the client's data to large data centers, which are remotely located, on which user does not have any control. If multiple providers cooperatively work together, the availability of resources can be increased. But still clients worry that whether their data is correctly stored and maintained by the cloud providers without intact. Hence, there is need of checking the data periodically for correction purpose which is called data integrity .In this paper we will discuss data integrity techniques that has been proposed so far, along with their pros and cons, like Proof of Retrievability (PoR), Provable Data Possession ( PDP) and a High Availability and Integrity Layer for cloud storage(HAIL).

References

[1]. Kan Yang ,XiaohuaJia, “Data storage auditing service in cloud computing: challenges, methods and opportunities”, DOI 10.1007/s11280-011-0138-0.

[2]. QianWang, CongWang, Kui Ren, Wenjing Lou and Jin Li, (2011). “Enabling Public Auditability and Data Dynamics for Storage Security in Cloud Computing,” 1045-9219/11/$26.00_2011IEEE.

[3]. G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson and D. Song, (2007). ”Provable Data Possession at Untrusted Stores”, Proc. 14th ACM conf. Computer and Comm. Security(CCS '07), pp. 598- 609,2007.

[4]. G. Ateniese, R.D. Pietro, L.V. Mancini, and G. Tsudi (2008). “Scalable and Efficient Provable Data Possession,” Proc. Fourth Int'l Conf. Security and Privacy in Comm. Networks (SecureComm'08), pp.1-10.

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

Current Issue Vol. 11 Issue 1

Most Read

Most Cited

Volume 1 Issue 1 November - January 2014

Cloud Security Requirements

Poonam Rawat* , Neha Rawat**, Shikha singh***, Awantika****

*-**** Students, Govind Ballabh Pant University of Agriculture and Technology, Pantnagar.

Rawat, P., Rawat ,N., Singh, S., and Awantika (2014). Cloud Security Requirements. i-manager’s Journal on Cloud Computing, 1(1), 1-5. https://doi.org/10.26634/jcc.1.1.2797

Abstract

References

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Throughput Variant Component Ranking In Dynamic FT Cloud Framework

Sudheshna tupadha* , Shoba Bindu C**

* Department of CSE, JNTUA College of Engineering, Ananthapuram, India ** Associate Professor & Head, Department of CSE, JNTUA College of Engineering, Ananthapuram, India

Sudheshna, T., and Bindu, C. S. (2014). Throughput Variant Component Ranking In Dynamic FT Cloud Framework. i-manager’s Journal on Cloud Computing, 1(1), 6-12. https://doi.org/10.26634/jcc.1.1.2798

Abstract

References

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Delegated Access Control In Public Clouds Using Two Layer Encryption

Malathi.P*

PG Scholar, Muthayammal College of Engineering, Rasipuram, Nammakkal (DT), Tamilnadu, India.

Malathi, P. (2014). Delegated Access Control in Public Clouds Using Two Layer Encryption. i-manager’s Journal on Cloud Computing, 1(1), 13-18. https://doi.org/10.26634/jcc.1.1.2799

Abstract

References

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Online Anomaly Based Intrusion Detection System Using Machine Learning

D.P. Gaikwad* , R.C. Thool**

* Assistant Professor, Department of Computer Engineering, All India Shri Shivaji Memorial Society’s College of Engineering, Pune. ** Professor, Head of Department of Information technology, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded

Gaikwad, D. P., and Thool, R. C. (2014). Online Anomaly Based Intrusion Detection System Using Machine Learning. i-manager’s Journal on Cloud Computing, 1(1), 19-25. https://doi.org/10.26634/jcc.1.1.2800

Abstract

References

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Techniques To Ensure Data Integrity In Cloud Survey

C.Sasikala* , Shoba Bindu C**

* Lecturer, Department of CSE, JNTUA College of Engineering, Anantapuramu, India. ** Associate Professor & Head, Department of CSE, JNTUA College of Engineering, Anantapuramu, India.

Sasikala, C., and Bindu, C. S. (2014). Techniques to Ensure Data Integrity in Cloud Survey. i-manager’s Journal on Cloud Computing, 1(1), 26-32. https://doi.org/10.26634/jcc.1.1.2802

Abstract

References

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Poonam Rawat* , Neha Rawat, Shikha singh*, Awantika****

* Department of CSE, JNTUA College of Engineering, Ananthapuram, India

** Associate Professor & Head, Department of CSE, JNTUA College of Engineering, Ananthapuram, India

* Assistant Professor, Department of Computer Engineering, All India Shri Shivaji Memorial Society’s College of Engineering, Pune.

** Professor, Head of Department of Information technology, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded

* Lecturer, Department of CSE, JNTUA College of Engineering, Anantapuramu, India.

** Associate Professor & Head, Department of CSE, JNTUA College of Engineering, Anantapuramu, India.