Cloud Computing Security project in python
Cloud Computing Security From Single To Multi-Clouds in python
The use of cloud computing has increased rapidly in many organizations. Cloud computing provides many benefits in terms of low cost and accessibility of data. Ensuring the security of cloud computing is a major factor in the cloud computing environment, as users often store sensitive information with cloud storage providers but these providers may be untrusted. Dealing with "single cloud" providers is predicted to become less popular with customers due to risks of service availability failure and the possibility of malicious insiders in the single cloud. A movement towards "multi-clouds", or in other words, "interclouds" or "cloud-of-clouds" has emerged recently. This paper surveys recent research related to single and multi-cloud security and addresses possible solutions. It is found that the research into the use of multi-cloud providers to maintain security has received less attention from the research community than has the use of single clouds. This work aims to promote the use of multi-clouds due to its ability to reduce security risks that affect the cloud computing user.
Cloud computing, or something being in the cloud, is a colloquial expression used to describe a variety of different types of computing concepts that involve a large number of computers connected through a real-time communication network such as the Internet.
Cloud computing is a term without a commonly accepted unequivocal scientific or technical definition.
In science, cloud computing is a synonym for distributed computing over a network and means the ability to run a program on many connected computers at the same time. The phrase is also more commonly used to refer to network-based services which appear to be provided by real server hardware, which in fact are served up by virtual hardware, simulated by software running on one or more real machines. Such virtual servers do not physically exist and can therefore be moved around and scaled up (or down) on the fly without affecting the end user—arguably, rather like a cloud.
The remainder of this paper is organized as follows. Section 2 describes the beginning of cloud computing and its components. In addition, it presents examples of cloud providers and the benefits of using their services.
Section 3 discusses security risks in cloud computing. Section 4 analyses the new generation of cloud computing, that is, multi-clouds and recent solutions to address the security of cloud computing, as well as examining their limitations. Section 5 presents suggestions for future work. Section 6 will conclude the paper.
SOFTWARE REQUIREMENTS:
OS : Windows
Python IDE : Python 2.7.x and above
Language : Python Programming
Database : MYSQL
HARDWARE REQUIREMENTS:
RAM : 4GB and Higher
Processor : Intel i3 and above
Hard Disk : 500GB Minimum
Setting up Software Environment
Python is a high level, interpreted, interactive and object-oriented scripting language.
Python is designed to be highly readable and has fewer syntactical constructions than other languages. Python is used in the development of this model. In this experiment, the following python libraries are used to develop the machine learning models:
• NLTK: It is a python package which works with human language data and provides an easy-to-use interface to different lexical resources like WordNet and text processing libraries. These lexical resources are used for classification, tokenization, stemming, tagging, parsing, and semantic reasoning [23].
• Pandas: It is a python package which acts as a data analysis tool and deals with data structures. Pandas carry out entire data analysis workflow in Python without having to switch to a more domain specific language like R.
• Tweepy: It is used in accessing the Twitter API by establishing the connection and to gather tweets from Twitter. This module is used to stream live tweets directly from Twitter in real-time.
• Numpy: NumPy is the fundamental package for computing with Python. It is used to add support to multi-dimensional arrays and matrices, with a large collection of high-level mathematical functions.
• scikit-learn: It is a simple and efficient tool for data mining and data analysis.
• matplotlib python library which generates plots, histograms, power spectra, bar charts, etc.
In this work matplotlib.pyplot module is used to plot the metrics.
• Gensim It is used to automatically extract semantic topics from documents, as efficiently as possible. Gensim is designed to process raw, unstructured text data. The algorithms in Gensim, such as Word2Vec where it automatically discovers the semantic structure of phrase by examining statistical co-occurrence patterns within a corpus of training documents. These algorithms are unsupervised. Once these statistical patterns are found, any plain text documents can be succinctly expressed in the new, semantic representation and queried for topical similarity against other documents.
• Keras: Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
·
Title
Page
·
Declaration
·
Certification
Page
·
Dedication
·
Acknowledgements
·
Table of
Contents
·
List of
Tables
· Abstract
CHAPTER SCHEME
CHAPTER ONE: INTRODUCTION
CHAPTER TWO: OBJECTIVES
CHAPTER THREE: PRELIMINARY
SYSTEM ANALYSIS
·
Preliminary
Investigation
·
Present System in Use
·
Flaws In Present System
·
Need Of New System
·
Feasibility Study
·
Project Category
CHAPTER FOUR: SOFTWARE
ENGINEERING AND PARADIGM APPLIED
·
Modules
·
System / Module Chart
CHAPTER FIVE: SOFTWARE AND
HARDWARE REQUIREMENT
CHAPTER SIX: DETAIL SYSTEM
ANALYSIS
·
Data Flow Diagram
·
Number of modules and
Process Logic
·
Data Structures and Tables
·
Entity- Relationship
Diagram
·
System Design
·
Form Design
·
Source Code
·
Input Screen and Output
Screen
CHAPTER SEVEN:
TESTING
AND VALIDATION CHECK
CHAPTER EIGHT:
SYSTEM SECURITY MEASURES
CHAPTER NINE:
IMPLEMENTATION, EVALUATION &
MAINTENANCE
CHAPTER TEN:
FUTURE SCOPE OF THE PROJECT
CHAPTER ELEVEN: SUGGESTION AND CONCLUSION
CHAPTER TWELE: BIBLIOGRAPHY& REFERENCES
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