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A generative adversarial network (GAN) is a framework composed of a generator and a discriminator. GAN learns the deep attributes without the need for hugely annotated training data. This learning is attained by back-propagation approach through competition between generator and discriminator network. Since 2014, GAN has been used in a wide variety of applications such as game development, security, image editing, data generation, attention prediction, and so on. In this article, we explore the fundamental working principle, architecture of GAN, and the effectiveness of GAN for video analytics. Video analytics has become a crucial research area for the academic world and the industry thanks to the availability of IP CCTV as well as the progressive growth in the video content analysis algorithms. Automation in video analytics has come into practice due to the extensive growth of deep learning algorithm. The prime intention of this chapter is to provide a comprehensive review of the GAN variants available for video analytics, explore different architectures, and further the techniques used in those respective applications.

Cloud computing provides storage for the multiple users to store and share their data anywhere at anytime basis. There were some security issues faced by the cloud users such as data correctness, data theft, data leakage, privacy on user level because of the third-party data control. One of the major issues in cloud storage is ensuring data integrity when data are shared by multiple users in the cloud and the data owner accesses data locally. To overcome this issue, many public integrity auditing schemes have been proposed where the computation overhead is huge for the data owner. Hence, efficient auditing with minimum overhead at client side is in need. In the proposed method, we have multi-user modification model with user revocation where the auditing work is delegated to a trusted third-party auditor (TPA) on a secure model, thereby reducing the overhead faced by client.

With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The computational grid is a new parallel and distributed computing paradigm that provides resources for large scientific computing applications. The main techniques that are most suitable to cope with the dynamic nature of the grid are the effective utilization of grid resources and the distribution of application load among multiple resources in a grid environment. This paper addresses the problem of scheduling and load balancing in a grid environment. A Decentralized Dynamic load balancing algorithm is proposed which combines the strong points of neighbor based and cluster based load balancing techniques. This algorithm estimates system parameters such as resource processing capacity, load on each resource and transfer delay for scheduling and load balancing. A set of simulation experiments show that the proposed algorithm provides significant performance over existing ones.

The increase in the number of security breaches has made information security risk management an essential security activity for all type of organizations. Risk Management involves assessment involves identification of assets, threats and vulnerabilities. Attacks by outsiders continue to cause the most security breaches to all organizations. Existing approaches like attack graph based risk assessment have scalability issues and focus on only single step attacks. It is very difficult to predict multistep attacks that exploit a chain of vulnerabilities. The multistep attacks are based on the causality of relation where every cause has an effect. Causality refers to a cause i.e. one event and consequences i.e. another event that has occurred because of the cause. The proposed system aims to make use of fuzzy cognitive maps to model the causally dependent events. Fuzzy cognitive map is a concrete representation of knowledge that can handle incomplete or conflicting information. This is very important in risk assessment because important information may be unreliable as they may be a result of unreliable measurement techniques. The proposed system will aid in proactive information security risk assessment.