International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 RNN with Adaptive Split Algorithm for the Analysis of Security on Cloud Computing 1Dr. K. Loheswaran, 2Dr. D. Murali, 3D. Kalaiabirami 1,2Associate Professor, Department of Computer Science and Engineering, CMR College of Engineering & Technology, Hyderabad - 501 401. Telangana. India 3Assistant Professor, Department of Computer Science and Engineering, Vivekanandha College of Engineering for Women, Tiruchengode, Tamilnadu, India Abstract One of the most recent trends in IT division is cloud computing (CC). It is an appropriated processing condition which has committed registering assets gotten to whenever from anyplace. In the present time, user keeps a high measure of information on cloud and even share a great deal of information, and thus, it is important to utilize safety efforts so that there is no risk to any of the client's information. To furnish an abnormal state of security with the fast headway of Internet, numerous devices and systems are being utilized. In this study, a Hybrid Adaptive algorithm is developed for the security on Cc. The proposed adaptive machine learning algorithm is the combination of Recurrent Neural Network (RNN) with adaptive split algorithm (ASA). RNN is one of the Artificial Intelligence (AI) techniques, which is utilized to classification purpose. The proposed hybrid adaptive algorithm is utilized to classify the data before the encryption process. Based on the process, the accuracy of the data is achieved and it requires less memory space only. After the data storage allocation, the design of end to end security framework is carried out. The main objective is to secure the data and eliminate the insider threats. The objective of the paper is to increase security by using adaptive split algorithm (ASA) for the transfer of data on cloud servers. The proposed hybrid adaptive method is implemented in JAVA platform and compared with the traditional methods, such as Artificial Neural Network (ANN), Support Vector Machines (SVM),respectively. Moreover, the statistical measures are evaluated Accuracy, Recall, Precision, F- measure, for the proposed and existing methods. Keywords: RNN, Adaptive split Algorithm, security, Cc, SVM and ANN 1. INTRODUCTION IT industries are pushing technology to a whole new level over time. With the elegance of IT, the Internet is one of the most popular technologies these days. It is on the verge of a revolution where resources are globally linked. Therefore, resources can be easily distributed and managed at anytime from anywhere [1]. CC [2] [15] [16] is a fresh computing model that offers organizations a novel commercial classic for adopting IT without prior investment. CC is everywhere now. In most cases, users are using the cloud unknowingly. Small and medium enterprises will move to CC because it supports quick access to their app and reduces the cost of infrastructure. CC is not only a technological solution, it is also a business model that can sell and rent computing power. The definition of CC from the National Institute of Standards and Technology (NIST) is that, on request, CC allows access to a ubiquitous and conveniently efficient network, with a set of configurable computing resources that can be delivered speedily and with least effort. And FIG. Management or service provider. Interference [4] [19]. CC is based on virtualization code, which means that there is only one large machine and more users are sharing this machine for their own dedicated resources. There are three levels of service [5]. One of them is Service Infrastructure (IaaS), which includes hardware tools such as network resources, hard disk, and memory. They are rented and charged according to usage. [17] [18] The second is the Platform as a Service (PaaS), which not only provides all the features as in IAS, but also offers operating system features and updates. [5] [20]. The third is Software as a Service (SaaS), 3191 ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 which is the best stretchy and easy to use. It has all the features of IaaS and PaaS, and offers the freedom to select software from a package of resources even now accessible [5] [11]. The main goal of CC is to reduce the computation problem and provide the highest security resources [14]. CC has many benefits, but there are also some challenges, such as security, trust, availability, interoperability and SLA. The user requests to provide their personal identification info, sensitive info, and usage. [6]. Three states that are functionally sensitive, below. ❖ The communication of personal responsive data to the cloud server, ❖ the cloud server communicate to clients computers with data and ❖ The client’s personal data’s are stored in cloud servers, it is remote server not held by the users. The above three CC-sensitive states are particularly vulnerable to security breaches, which conduct research and examination of the security aspects of CC training [7]. Critical requires strong storage, management, sharing and verification of complex data. Establishing patterns and trends to improve the quality of health care is the best way to protect the country and find an alternative. Therefore clouds that need to be fixed are very important [8]. Recently, the network has been integrated with Cloud Machine Learning (ML) for cloud security [9]. In the field of ML, data classification is a method of classifying the classification of unclassified data samples set by the construction classifier [10]. Data mining can be considered as one of the most important things to find knowledge from big data. Different algorithms and techniques are available for data exploitation. Classification techniques can be used in big data to find the mine rule [13]. The propaganda backbone used by ANN is a productive and viable way to guide collaborative learning (CL). The accuracy of back-propagation neural networks depends on the quantity and quality of the data used for the study. CL improves learning accuracy by integrating more number of data sets into the process of learning, compared to learning only with the local data set. CL has become more accessible due to computing architectures such as CC [12]. To incorporate CL over the Internet, it is necessary to provide a solution to allow unreliable parties to conduct common studies on the neural network without exposing their private data sets [12]. This approach is a major challenge and has some drawbacks. Security data sets are very rare, especially due to privacy concerns. Here, machine learning models are trained and evaluated in experimentally generated datasets with insufficient wealth [9]. Therefore, in the proposed technique, we integrated RNA with Adaptive split Algorithm (ASA). RNN is one of the AI techniques used for classification purposes. The proposed hybrid adaptation algorithm is used to classify data before the encryption process. After allowing data storage, the end-to-end security framework will be designed. Therefore, the main purpose of the proposed technology is to reduce data security and penetration. The rest of the papers follow, because section 2 represents a recent literature review, and section 3 defines the proposed methodology. Analysis of the proposed method is displayed in Section 4. Lastly, the concluding section is mentioned in Section 5. 2. Literature Review: A Recent Analysis Lot of research works are presented in the research area of Security on Cc. Some of them are reviewed here, Chiba et.al [21] proposed a cooperative and hybrid network intrusion detection scheme to detect network attacks in the cloud environment by observing network traffic with high service quality and performance. To detect unknown attacks, they have utilized Snort as signature and Back Propagation Neural Network is used when detecting anomaly to reduce the detection time. As the BPN has the disadvantage of slow convergence, optimization algorithm is used to optimize the factors in order to increase the convergence rate. Their proposed technique provides low computational cost, high detection rate and accuracy. 3192 ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 Victor and Muthu [22] established a framework known as CC Adoption Framework (CCAF), which was customized to secure cloud data. This report describes the overall image, motivation, and CCAF components for data protection. They used a data center with 10 megabytes of data and imitated the Business process modeling nation (BPMN) to learn how to use the data. The use of the BPMN simulation allowed for the evaluation of selected safety performances before the actual implementation. They demonstrated that multi-layer CCAF security can guard data in real-time and that there are three security layers: 1) firewall and access control; 2) characteristics management and intrusion anticipation and 3) convergent encryption. For CCAF validation, infiltration testing using 10,000 Trojans and viruses involves two groups of ethical hacking experiments. Multi-layer CCAF security able to kill 9,919 viruses and Trojans in a matter of seconds, and the rest can be determined or isolated. Tests have shown that the injection of viruses and Trojans can reduce the percentage of barriers to succession, but 97.43% of them can be shipped. CCAF can be much effective while combined with BPMN model to calculate the security practice and penetrate the test outcomes. For these users, a method proposed by Dong et.al [23] does not reveal the actual model data. Data encryption and Feed-forward propagation are joined in a single process: the first layer of the deep network is migrated to the users' local devices and the nearby workflow, followed by the drop unit's method output method, which can be used to reverse the output. The approach fulfills the privacy requirements of envy and confirms many advantages over the traditional encryption / decryption approach.