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Security issues with cloud computing
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Evolutionary Algorithm and Discussions
Cloud computing provides variety of internet on demand services such as software, infrastructure and data storage. for the purpose of provision of private service to the user, there is possibility to use multi-level password creation and documentation or authentication techniques.
This technology assists in creating the password in several levels of the company. So, that the strict documentation and authorization is possible. The levels of security in cloud may be more developed by multi-levels documentation.
There are differences between evolutionary algorithms and traditional algorithms in the search for information and use it to guide the search. In the algorithms of conventional research be sequenced and is in transition from one point to the next.
Either in evolutionary algorithms, the search process is performed in parallel from which to explore the different regions in the search space is the most important characteristic of evolutionary algorithms possibility of including prior knowledge in the area of the problem and the possibility of using traditional search algorithms to side. Despite these advantages, however, evolutionary algorithms are considered too slow.
In addition to being not guarantee access to the optimal solution at a specific time; using evolutionary computing in many areas , for example but not limited to : information retrieval and data mining and bioinformatics Bio-informatics and e-Learning.
Cloud computing is considered the modern technologies that rely on the Internet on demand and provide a wide range of online services: for example, software, data storage and infrastructure.
This technology has been used by a huge number of users worldwide in order t...
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...ll be obtained by the probability of
SSS which will be located as P (E) and to be presented as P (E) = p3
Breaching Failure in multi-levels documentation is as follow:
1-P (E) = 1- p3.
For example, If the possibility of the success in any level is
p=0.1
in this case the possibility of breaching in multi-levels (3 levels) documentation is 0.001
Accordingly, we deduce that by using multi-levels documentation systems, we could also develop the security file.
In multi-levels techniques, each individual user will have a encumbrance to remember just one password.
According to what above mentioned, we anticipate that the usage of multi-levels documentation is to provide better security as a service on demand for the purpose to access the cloud service.
Kerberos provides a secure authentication scheme. Authentication is needed to restrict the intruders and malicious users. The major security issues discussed are privacy of the data, integrity of data and authentication mechanism which is not there in Hadoop. Hadoop supports Kerberos for authentication and many security features can be configured with the Hadoop to restrict the accessibility of the data. The data can be associated with the user names or group names in which data can be accessed. Kerberos is a conventional authentication system, improved authentication systems can be used which are more secure and efficient than
Cloud is the result of a decade research in the field of distributing computing , utility computing, virtualization , grid computing and more recently software, network services and web technology which is changeable evolution on demanding technology and services also as looking to the rapid growth of cloud computing which have changed the global computing infrastructure as well as the concept of computing resources toward cloud infrastructure. The important and interest of cloud computing increasing day by day and this technology receives more and more attention in the world (Jain, 2014) the mostly widely used definition of cloud computing is introduced by NIST “as a model for enabling a convenient on demand network access
Created by Philip Zimmermann in 1991, this program has been widely used throughout the global computer community to protect the confidentiality and integrity of the users’ data, giving them the privacy of delivering messages and files only to their intended individual or authorized person (Singh, 2012). Not only being useful for individuals as a privacy-ensuring program, it has also been used in many corporations to protect their company’s data from falling into the wrong hands (Rouse, 2005).
In view of emergence in cloud computing and cloud based identity management providers, the need for implementing SAML protocol is imperative. In addition, with the proliferation of SaaS (Software as a Service), and other web based applications, identity management has become challenging for various enterprises. Handling so many usernames and passwords for your intranet, cloud, webmail, HR system, and other resources is nothing but bothersome especially when your workforce is huge. This is where SAML is desperately needed. Many hosted services providers support SAML for authentication including Google Apps, Salesforce.com, Zendesk and Zoho. Thousands of large enterprises have adopted it as their standard protocol for their communicating identities across their network environments.
SecurID is based on password and pin, a double layered access authentication principle. This technology is noted to have a more reliable level of user passwords. The cryptographic technology has the ability to automatically changes passwords every 60 seconds. The top benefit of SecurID helps positively identify users before they access critical confidential data systems. Each authenticator possesses a special symmetric key that is combined with an algorithm to create rapid one-time passwords (OTP). The OTP’s are stored in the Authentication Manager server for optimal security. OTP’s are established and known to the user – the PIN acts as a back-up layer which makes it extremely difficult for hackers to exploit. Strengthening vulnerabilities in access control mechanism with a layered technology, makes SecurID access keys a worthwhile product.
Genetic Algorithms provide a holistic search process based on principles of natural genetics and survivals of the fittest……
Based on the evolutionary theory as described above, evolutionary computation produce solutions and keeping the fittest of them. In 1948, Alan Turing was the first who introduce this biological approach in problem solving. On 1962, we had the first actual experiments on "optimization through evolution and recombination".
When they wanted to save photos online instead of on your personal computer, they are able to use “cloud computing” service. Cloud computing means that the transfer of computing data or information over the internet. Not just to keep data in your personal computer, they are able to save the data on internet server to open their data in any computer. In this report we will walk through about what is cloud computing, what kinds of model did cloud computing have, types of cloud computing, benefits of cloud computing, and security.
Different objective functions generate different solutions even form the same evolutionary algorithm. Presuming also that the fitness could either be a minimization or a maximization function. Moreover, the algorithm could be formulated with one or with multi objective functions. To sum up, "choosing optimizati...
Fogel, D. (2009) Artificial intelligence through simulated evolution. Wiley-IEEE Press. Available at: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5311738 (Accessed: February 09, 2014).
The evolutionary theory is the concept that species evolve over time through the mechanism of natural selection of survival and reproduction. Natural selection means acting on the assumption that various living organisms were produced by genetic diversity and mutation. The evolution theory may also be referred to as the philosophizing science. This theory states that all phenomena are derived from natural causes and can be explained by scientific laws without reference to a plan or purpose.
Despite the numerous advantages offered by cloud computing, security is a big issue concerned with cloud computing. There are various security issues and concerns associated with cloud computing, among them being phishing, data loss and data privacy. There are different mitigation measures that cloud pioneers are currently using to ensure data stored in the cloud remain secure and confidential as intended. Encryption is one mitigation method used to ensure security in cloud computing. According to Krutz and Vines (2010), encryption involves coding of the data stored in the computing cloud such that hackers cannot gain access to the data. Data encryption seems to be the most effective method of ensuring security in computing (Krutz and Vines, 2010). However, it is of paramount importance to note that encrypted data is usually difficult to search or perform various calculations on it.
With computers and technology rapidly evolving, staying relevant is becoming more and more difficult to companies in the information technology industry. Cloud computing is a rising technology that could possibly be a great help or a burden to the IT infrastructure. With many new technologies, businesses wonder whether it is profitable enough to invest in such a technology and whether the company will benefit greatly from it. Although cloud computing is an emerging technology, it will come with risks such as security vulnerabilities, but could also be a great cost effective measure that will help out an organization’s infrastructure. Although a new technology, cloud computing is very advantageous for organizations and companies.
Optimization, in simple terms, means minimize the cost incurred and maximize the profit such as resource utilization. EAs are population based metaheuristic (means optimize problem by iteratively trying to improve the solution with regards to the given measure of quality) optimization algorithms that often perform well on approximating solutions to all types of problem because they do not make any assumptions about the underlying evaluation of the fitness function. There are many EAs available viz. Genetic Algorithm (GA) [1] , Artificial Immune Algorithm (AIA) [2], Ant Colony Optimization (ACO) [3], Particle Swarm Optimization (PSO) [4], Differential Evolution (DE) [5, 6], Harmony Search (HS) [7], Bacteria Foraging Optimization (BFO) [8], Shuffled Frog Leaping (SFL) [9], Artificial Bee Colony (ABC) [10, 11], Biogeography-Based Optimization (BBO) [12], Gravitational Search Algorithm (GSA) [13], Grenade Explosion Method (GEM) [14] etc. To use any EA, a model of decision problem need to be built that specifies: 1) The decisions to be made, called decision variables, 2) The measure to be optimized, called the objective, and 3) Any logical restrictions on potential solutions, called constraints. These 3 parameters are necessary while building any optimization model. The solver will find values for the decision variables that satisfy the constraints while optimizing (maximizing or minimizing) the objective. But the problem with all the above EAs is that, to get optimal solution, besides the necessary parameters (explained above), many algorithms-specific parameters need to be handled appropriately. For example, in case of GA, adjustment of the algorithm-specific parameters such as crossover rate (or probability, PC), mu...
The convergence of the GA to a suitable solution depends on its basic parameter like reproduction, crossover, mutation, selection and population; which to find a relationship among them to maintain search robust...