Cloud Security Requirements

Vol.1  No.1

Year : 2014

Issue : Nov-Jan

Title : Cloud Security Requirements

Author Name : Poonam Rawat, Neha Rawat, Shikha singh, Awantika

Synopsis :

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.

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Delegated Access Control In Public Clouds Using Two Layer Encryption

Vol.1  No.1

Year : 2014

Issue : Nov-Jan

Title : Delegated Access Control In Public Clouds Using Two Layer Encryption

Author Name : Malathi.P

Synopsis :

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.

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Throughput Variant Component Ranking In Dynamic FT Cloud Framework

Vol.1  No.1

Year : 2014

Issue : Nov-Jan

Title : Throughput Variant Component Ranking In Dynamic FT Cloud Framework

Author Name : Sudheshna tupadha, Shoba Bindu C

Synopsis :

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.

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Techniques To Ensure Data Integrity In Cloud Survey

Vol.1  No.1

Year : 2014

Issue : Nov-Jan

Title : Techniques To Ensure Data Integrity In Cloud Survey

Author Name : C.Sasikala, Shoba Bindu C

Synopsis :

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).

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Online Anomaly Based Intrusion Detection System Using Machine Learning

Vol.1  No.1

Year : 2014

Issue : Nov-Jan

Title : Online Anomaly Based Intrusion Detection System Using Machine Learning

Author Name : D.P. Gaikwad, R.C. Thool

Synopsis :

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.

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