Cloud computing offers a technological revolution to the end-users need less infrastructure costs with virtualizes resources, and storage remains the insecure to delivers the scalability. The most common type of Distributed Denial of Service DDoS attack, (denial of service), is a serious damage measure that affects virtual cloud users and Internet Service Providers (ISPs) are predominantly affects ongoing service attacks. I'm the recipient. These legacy of machine learning approach used to detect vulnerabilities to the attacker's leading network traffic intervention opening the door. By concentrating feature selection and classification approach with optimized neural network model to detect the DDoS type monitoring. This presents a deep neural network based DDoS detection system using Subset Feature Selection based Cascade Correlation Optimal Neural Network (SFS-C2ONN). The proposed approach is based on assumptions based on flow rate which is collected as dataset previously extracted from a model for network traffic. The test results shows that the sensitivity and specify based calcification approach which is suitable for the detection of neural network architecture and hyper parameters, and the optimizer DDoS attack. The results are obtained by calculating the accuracy of the attack detection.

The neural network is one of the best data mining techniques that have been used by researchers in different areas for the past 10 years. Analysis on Indian stock market prediction using deep learning models plays a very important role in today's economy. In this chapter, various deep learning architectures such as multilayer perceptron, recurrent neural networks, long short -term memory, and convolutional neural network help to predict the stock market prediction. There are two different stock market price companies, namely National Stock Exchange and New York Stock Exchange, are used for analyzing the day-wise closing price used for comparing different techniques such as neural network, multilayer perceptron, and so on. Both the NSE and NYSE share their common details, and they are compared with various existing models. When compared with the previous existing models, neural networks obtain higher accuracy, and their experimental result is shown in betterment compared with existing techniques.

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A new technique using fuzzy in a recursive fashion is presented to deal with the Gaussian noise. In this technique, the keyframes and between frames are identified initially and the keyframe is denoised efficiently. This frame is compared with the between frames to remove noise. To do so the frames are partitioned into blocks; the motion vector is calculated; also the difference is measured using the dissimilarity function. If the blocks have no motion vectors in the block, the block of value is copied to the between frames otherwise the difference between the blocks is calculated and this value is filtered with temporal filtering. The blocks are processed in overlapping manner to avoid the blocking effect and also to reduce the additional edges created while processing. The simulation results show that the peak signal to noise ratio of the new technique is improved up to 1 dB and also the execution time is greatly reduced.

Access control has made a long way from 1960s. With the advent changes of technologies pertaining to location transparency in storage of data, there arises different access control scenarios. Cloud storage, the predominant storage that is being in use currently, also paves way to various access control problems. Though there are various access control mechanisms such as RBAC, ABAC, they are designed on the user's perspective such as the role held by the user or other attributes assigned to the user. A new access control mechanism called object relationship based access control (RoBAC) has been developed based on the relations held among the users. The policy decision of access control is based on the relationship among the classes followed in the Java programming. Results have shown that this model best suits various scenarios in the cloud environment, and it also shows that the time for making decision either to allow or to deny is reduced compared to the existing system.