This paper presents the survey on data storage and retrieval in cloud computing. In this paper the study on scope and security issues related to data storage and information retrieval in cloud computing is done. Data storage and retrieval with data security is typical issue in today's scenario. The main objective of cloud computing is to enables users with limit computational resources to outsource their large computation workloads to the cloud. The integrity and confidentiality of the data uploaded by the user is ensured doubly by not only encrypting it but also providing access to the data only on successful authentication. In this paper the different security problems existing in the cloud was identified and solutions for the same have suggested INTRODUCTIONCloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction". Cloud Computing is a completely new Information Technology and it is known as the third revolution after PC and Internet in IT. The basic principle of Cloud Computing is collecting large quantities of information and resources stored in personal computers, mobile phones and other equipment, Cloud Computing is capable of integrating them and putting them on the public cloud for serving users. Cloud computing technology redefines the advances in information technology. Cloud computing is an @ IJTSRD | Available Online @ www.ijtsrd.com | Volume -2 | Issue -4 |

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Intrusion Detection System (IDS) is a vital component of any network in today's world of Internet. IDS are an effective way to detect different kinds of attacks in interconnected network. An effective Intrusion Detection System requires high accuracy and detection rate as well as low false alarm rate. To tackle this growing trend in computer attacks and respond threats, industry professionals and academics are joining forces in order to build Intrusion Detection Systems (IDS) that combine high accuracy with complexity and time efficiency. With the tremendous growth of usage of internet and development in web applications running on various platforms are becoming the major targets of attack. Security and privacy of a system is compromised, when an intrusion happens. Intrusion Detection System (IDS) plays vital role in network security as it detects various types of attacks in network. Implementation of an IDS is distinguishes between the traffic coming from clients and the traffic originated from the attackers or intruders, in an attempt to simultaneously mitigate the problems of throughput, latency and security of the network. Data mining based IDS can effectively identify intrusions. The proposed scheme is one of the recent enhancements of naive bayes algorithm. It solves the problem of independence by averaging all models generated by traditional one dependence estimator and is well suited for incremental learning. Empirical results show that proposed model based on SADE is efficient with low FAR and high DR.

The requirement to perform complicated statistic analysis of big data by institutions of engineering, scientific research, health care, commerce, banking and computer research is immense. However, the limitations of the widely used current desktop software like R, excel, minitab and spss gives a researcher limitation to deal with big data and big data analytic tools like IBM BigInsight, HP Vertica, SAP HANA & Pentaho come at an overpriced license. Apache Hadoop is an open source distributed computing framework that uses commodity hardware. With this project, I intend to collaborate Apache Hadoop and R software to develop an an platform that stores big data (using open source Apache Hadoop) and perform statistical analysis (using open source R software).Due to the limitations of vertical scaling of computer unit, data storage is handled by several machines and so analysis distributed over all these machines. Apache Hadoop is what comes handy in this environment. To store massive quantities of data as required by researchers, we could use commodity hardware and perform analysis in distributed environment.

The ubiquitous computing and its applications at different levels of abstraction are possible mainly by virtualization. Most of its applications are becoming pervasive with each passing day and with the growing trend of embedding computational and networking capabilities in everyday objects of use by a common man. Virtualization provides many opportunities for research in IoT since most of the IoT applications are resource constrained. Therefore, there is a need for an approach that shall manage the resources of the IoT ecosystem. Virtualization is one such approach that can play an important role in maximizing resource utilization and managing the resources of IoT applications. This paper presents a survey of Virtualization and the Internet of Things. The paper also discusses the role of virtualization in IoT resource management.

With the explosive growth in popularity of social networking and messaging apps, online social networks (OSNs) have become a part of many people's daily lives. There are many mental disorder encountered noticed of social network mental disorders (SNMDs), the basic parameter at which evaluate the mental level of user such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed day by day, resulting in delayed clinical intervention. In this work, the mining online social behaviour provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, now propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD), that exploits features extracted from social network data log file to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMDD based Markov Model (SMM) to improve the accuracy. To increase the scalability of SMM, we further improve the efficiency with performance guarantee. Our framework is evaluated via user study with 3126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.