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With the increasing demand for high availability, scalability and cost minimization, the adaptation of cloud computing is also increasing. By the demand from the data, consumer or the customers of the applications, the service providers or the application owners are migrating all the applications into the cloud. These migrations of the traditional applications and deploying new applications are benefiting the consumers and the service providers. The consumers are getting the higher availability of the applications and in the other hand, the consumers of the applications are getting benefits from of the cost reduction by optimal scalability and deploying additional features with the least cost, which intern providing the better customer satisfaction. Nevertheless, this migrations and new deployments are attracting the attention of the hackers and attackers as well. In the recent past, several attacks are reported on various popular services like search engines, storage services, and critical application ranging from healthcare to defence. The attacks are sometimes limited to the data exploration, where the attackers only consume the data and sometimes the attackers destroy crucial services. The major challenge in detecting these attacks is mostly identifying the nature of the connection request. Also, identifying the attacks are not sufficient in providing the security for the cloud services and must be deployed as security as a service in the applications or the services or in the data centre as automatic and continuous measures. Various research endeavours have shown critical enhancements in the ongoing past for recognizing the security attacks. Nonetheless, these attempts have not provided any solution in preventing the security attacks. Also, the existing methods as mentioned are not automated and cannot be included in the services. Thus, this work provides a unique automated framework solution for detecting the application traffic pattern and generates the rule sets for detecting any anomalies in the request types. The major outcome of this work is to identify the attack types and prevent further damages to the cloud services with a minimal computational load. The additional benefits from this work are the preventive measure for popular attack types. The work also demonstrates the ability to detect a new type of attacks based on traffic pattern analysis and provides preventive measures for making the cloud computing application hosting industry a safer place.

Abstract

In the field of Aquaculture with the help of digital advancements huge amount of data is constantly produced for which the data of the aquaculture has entered in the big data world. The requirement for data management and analytics model is increased as the development progresses. Therefore, all the data cannot be stored on single machine. There is need for solution that stores and analyzes huge amounts of data which is nothing but Big Data. In this chapter a framework is developed that provides a solution for shrimp disease by using historical data based on Hive and Hadoop. The data regarding shrimps is acquired from different sources like aquaculture websites, various reports of laboratory etc. The noise is removed after the collection of data from various sources. Data is to be uploaded on HDFS after normalization is done and is to be put in a file that supports Hive. Finally classified data will be located in particular place. Based on the features extracted from aquaculture data, HiveQL can be used to analyze shrimp diseases symptoms.