RNN with Adaptive Split Algorithm for the Analysis of Security on Cloud Computing

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),

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.

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.

In [24] Ismail et al., he proposed an integrated method for classifying and securing big data before conducting duplication, data mobility and enquiry. The need to ensure the mobility of big data is determined by dividing the data into two categories according to the impact level of their content risk Confidential and public. Hadoop's distributed file system playback architecture was used to divide the file into disjoint fragments and allocate it among various maps for easy inspection of sensitive data. Based on this, the classification technique identifies the files that need to be secured and lessens the additional price of applying public file data security, which improves the performance of the cloud system. Extensive investigational analysis was performed on real-world applications in CC systems, which indicated that high performance can be attained with method of security and classification. An adaptive method for incorporating security constraints should be considered during the classification of big data.

Mahmoud et.al [25] presented an efficient approach to detecting malware in the cloud infrastructure using the deep learning approach Convolution Neural Network (CNN). Firstly, the 2D CNN standard was used by training the available metadata for each of the processes in a virtual machine (VM) received with the help of the hypervisor. The CNN Classifier improves the accuracy of a new CNN 3d novel, which greatly reduces the chance of missing samples in data training and collection. Experiments were performed on data together by running different malware on VMs. The malware used in the experiments was randomly selected, which greatly activates the selection bias of known malware for easy detection. Also the proposed 2d CNN model reached an accuracy of ≃ 79%, and 3d CNN model considerably develops the accuracy to ≃ 90%.

In this paper, we use RNN with adaptive partitioning algorithm for cloud security analysis. The main contribution of the paper is to provide optimal security and eliminate special benefits. Give access to non-malicious users only, and select only those resources that provide the best quality services to customers. A detailed description of the projected hybrid adaptation method is described in Sec. 3.

3. PROPOSED ADAPTIVE TECHNIQUE FOR SECURITY IN CC Here a hybrid technique is proposed for the security on Cc which is constructed by combining RNN with ASA. RNN is one of the AI techniques, which is utilized to classification purpose. The working flow of the proposed technique is given Fig.1. Initially the incoming data comes from users are subjected to intruder detection. To detect whether intruder present in the data, the artificial intelligence 3193 ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 based RNN classifier is utilized. After filtering the intruders from the data, the non-corrupted data is subjected to encryption process to increase security while transferring the data. AES-ASA is utilized in the process of encryption. After encryption, the secured data is transferred over the cloud.

user Data classification Data encryption Third Secure message flow party auditor user Data classification Data encryption

Data flow

user

Cloud storage

Secure message flow

Data service provider

Fig.1: Working Flow of thee Proposed RNN-ASA Technique

3.1.Data Classification Using RNN

The intruders or malicious information must be removed from the input data taken by the user to transfer over the cloud. We have used RNN classifier here to classify the malicious information present in the data. After the classification, the malicious data is removed so that the information is secured. The architecture of RNN classifier is given in figure.2 and the process of classifying the information using is detailed below:

Among the neural models, RNN are one of the most popular architectures used in NLP problems because their recurrent structure is very appropriate to process the big data. In sequence data, this is mostly statistically forward and backward. In terms of time series requirements and training data, the major goal is to extract and learn the rules for completing and predicting output data provided by the test input data. Inputs and outputs, both can be continuous and categorical variables, or both. In continuous outcomes, the issue is defined as "regression" and classification as "regression". In this article, we use the term prediction as a general term for regression and classification and classify the issue as a classification difficult. Fig. 2 shows the architecture for the RNN vectors, one for each of the input vectors in the RNN input layer.

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204

Output layer O n

hdn-1 hdn Hidden layer

Input layer In

Fig.2:Basic Architecture of RNN Classifier

The purpose of accurate classification or regression process the input data is much useful. RNN can be achieved by delaying production in a timely manner to take into account future info.

Let deliberate the input sequence I i0 ,i1,...,iN 1,

Recurrent layer hidden states is assumed by Hd hd 0 ,hd1,...,hd N1

Output values of the output layer O  o0 ,o1,...,oN1 . Output and hidden value is ruled by

hd N  FWihdiN Whdhdhd N1  bhd  (1)

oN  OWhdohd N  bo (2)

The training process of RNN is close to the exercise of a neural network of feed forward, because the factors are shared so that the gradient takes dimensions of two basic. The present time step and Where

Wihd ,Whdhd andWhdoare the input-hidden, and hidden-output weights correspondingly. Here, the output and hidden layers of the squashing function of F and O.RNN allow the distribution of parameters across each layer. The current sharing implementation removes the total count of factors that need to be adjusted.

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 3.1.1 Propagation through Bidirectional Long Short Term Memory (LSTM)

Fig.3: Working Procedure of Bidirectional LSTM

RNNs have the ability to capture skill of mapped sequences, and these sequences have a things known as input and output alignment. RNN has shown the accuracy and success of learning knowledge on certain issues, and there is a short time lag between the answers they need and the capability to accurately model and manage data processing sequence. To resolve this issue, this network architecture uses a node called the Constant Error Carousel (CEC), which allows the propagation of a fixed error signal over time. Subsequently, LSTM controls the carousel access with constant error using multiplication gates. The goal of the LSTM model revolves around what we call a memory cell (M), which mainly transforms and hides the input knowledge done period. M is handled by sigmoid-based functioning gates in practice. The gates decide whether the LSTM is still hiding and forgetting the value from the gate. LSTM is the central part of keeping information from entries that have already passed through the hidden state. Here, we will operate our inputs in two ways: one from the past to the future and the other from the future. This is different from the unified direction. Hidden. Combined here, we can keep info from the future and past together at any time. BLSTM is ten times cheaper than LSTM. Here, BLSTM is a RBNN with hidden layers, hence it is called LSTM cells. These types of networks can process a limited number and predict many labels based on the module's past and future framework.

The output O obtained from the RNN consists of classified data. Based on the classification, the  malicious data are removed from the input data I and it is denoted as I . After removal of malicious data, the non-corrupted data I is subjected to encryption process with the help of AES and Adaptive Split Algorithm to enhance the security of data while transfer over the cloud.

3.2 Enhance Security using Adaptive Split Algorithm Encryption is a process to make hide information keep it as a secret information. The actual process of cryptography is called as encryption. Converting or transforming the data into some various format so that data appears to be worthless and should be unrecognizable. In other ways, it can define the process of converting plaintext to cipher text where plaintext act as the input to the encryption process and cipher text as the encryption process of output.

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 In the proposed technique we have combined the well-known AES and Split Algorithm. AES algorithm is used for encryption and split algorithm is used for splitting of data into multiple portions which is then encoded and stored it on a different cloud. AES was published by NIST. It uses 128 -bit block size with the 128, 192 or 256 bits key size. Asymmetric algorithms use different keys. Nowadays, no attack against AES exists. Therefore, AES is considered as one of the most preferred encryption standard high security systems around the world. The non-corrupted data obtained from the classification process I is fed as the input for encryption process using AES. The detail procedure of AES is given in [27].

In data splitting and clubbing algorithm, we are will be splitting the data into different portions then encryption of those split units are to be done and then store it on different cloud server by sharing over the cloud to another user. Data clubbing or merging can be done with another data portion. The password is used here by AES to encrypt the different data portions with a unique number that creates an encrypted data. Now the same password is used to decrypt the data so as to enable the feature of maximum security. The encryption is done through the following steps:

1) Start the process 2) Input data and password is accepted 3) The random number which is generated from the password is unique, as that will serves as a key 4) File and key are split into n parts. 5) The First split of the key is used to encrypt the first split of the data, the second split of the key to encrypt the second split of the data and so on. 6) All splits are combined to get final data 7) Process is stop

The two mechanisms that are being followed here to encrypt the data are split algorithm then AES to provide data security in cloud storage. The function of preventing data from being hacked is done by the split algorithm into different data units. Necessary security is provided by AES before uploading the data.

4. Results and Discussion The RNN-ASA algorithm was executed in 64 processing bit in Windows 10 operating system and 16 GB RAM and a Pentium(R) processor with CPU 2.40GHz frequency range using tools WEKA and JAVA. The proposed algorithm is compared with the conventional Classification techniques such as Support Vector Machine (SVM), BPNN and RNN-LSTM. The presentation of the proposed system is calculated against the hyper-parameters of RNN. The comparison standards are accuracy, Precision (P) Recall, (R) and F-Measure (F) with learning rate. Time steps for analyzing the variation of the behavior of the model with an update on hyper parameters.

Correctly predicted as normal parameter is True Positive (TP) Correctly predicted as an attack parameter is True Negative (TN) The examples identification is False Positive (FP) The cases are prefigured as normal parameter is False Negative (FN) Accuracy is calculated based on number of correct predictions. Recall (R) is defined as the ratio of TP records over the sum of TP and FN. Precision (P) is defined as the ratio of TP records over the sum of TP and FP F-measure (F): is defined as the harmonic mean of both R and P

The results are the initial one is a binary classifier (abnormal/normal) and the second one is 5-class classifier (categories of normal and 4 abnormal).

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 Experiments with the NSL-KDD dataset have carefully evaluated the feasibility of the expected technology. This is an updated version of the KDD99 data set, and the KDD99 data set contains redundant records in the training and testing data set. NSLKDD classifies two classes: normal and defective. There are 125,975 records of training data and 22544 records for test data.

4.2 Discussion The proposed algorithm compares different performance techniques. Comparison outcomes are accurately prepared to detect attacks in the NSLKDD test dataset. Classification results compared with existing classification techniques such as SVM and BPNN. Figure 7 represents the proposed algorithm of the ROC curves and the NSL-KDD of conventional techniques, without selecting features in the test data.

On looking at the Fig.5 and 6, the proposed technique with BLSTM provides better performance when compared with the other techniques such as SVM, RNN-LSTM, BPNN and RNN in both classifications. The performance measures obtained at different time steps 15,25,35,55. In figure.6, we have shown the graphical representation of accuracy of the techniques SVM, BPNN, RNN-LSTM, RNN and Proposed RNN-BLSTM. The accuracy of the proposed technique is greater than that of the conventional techniques which implies that the proposed technique performs better than other techniques. Similarly the ROC curve plotted against TPR and FPR of the above mentioned techniques given in Fig.7 shows that the proposed technique provides better rate of TPR. In figure.8, we have shown the time taken by the techniques for encryption process.

Fig.4: (I) Precision, (II) Recall, (III) F-Measure of various RNN Propagation model in Binary classification

Performance measures such as precision, recall and f-measures are calculated for the proposed technique using binary classifier at different time steps 15, 25, 35, 45 and 55 and it is compared with the

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 other techniques such as SVM, RNN-LSTM and BPNN. On looking at the figure 4, the recall rate is higher for the proposed technique than that of the conventional techniques which implies that the proposed technique predicted the false data effectively. Even though the precision rate is higher for SVM than our technique, the recall rate is higher for the proposed technique. There is a fluctuation in graph when time step increases. In future work, need to be work to increase the precision rate.

Fig.5: (I) Precision, (II) Recall, (III) F-Measure of various RNN Propagation model in 5-class classification

Similarly, recall, precision and F-measures are calculated for the proposed technique using 5-class classifier at different time steps 15, 25, 35, 45 and 55 and it is compared with the other techniques such as 3199 ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 SVM, RNN-LSTM and BPNN. The measured values are plotted in graph which is given in figure 5. The precision rate is fluctuating when compared to recall and f-measure when the time step increases. Here also the recall rate is higher for the proposed technique than the other techniques.

Fig.6: Comparison of proposed ASA ROC with that of conventional Techniques

In fig.6, accuracy of the proposed technique is evaluated and it is compared with the conventional techniques such as SVM, BPNN, RNN-LSTM and RNN. The accuracy of the proposed technique only reached above 90% while the accuracy of the other techniques lies behind 90%. The accuracy of the our technique is 7-12% greater than that of the conventional techniques. Hence the proposed technique performs better than the other techniques and it can be used for real time implementation

Fig.7:Comparison of accuracy of the proposed technique with conventional classifiers In CC services, authentication is a weak point that an attacker routinely targets. One-day authentication is now used in simple username and password-based knowledge-based authentication, but recently financial institutions have used different forms of secondary authentication, making them more vulnerable to popular phishing attacks. We analyzed here the man's cryptographic attacks in the medium. A Man-In-The-Middle attack intercepts and relays messages on a public key exchange for attackers, but also replaces their own public key for the one invited by the attacker, so that the two unique parts seem to communicate with each other. . In the process, the two real parts seem to communicate normally. The sender does not recognize that the receiver is an unknown attacker who bothers to access or change the message before relaying it to the recipient. After that, the session is placed between the host PC and the web server, and the attacker can gain some of the session organization by capturing the cookies used to set the session.

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 By utilizing the proposed method, the hacking percentage of the MIM attack is analyzed and tabulated in the table 1. For the different file size, the attack is happened and the hacking percentage is analyzed. The proposed method has achieves less percentage 7.28% while comparing other methods such as SVM and ANN. Similarly, the Key breaking time also analyzed and tabulated in table 2. While using the proposed method, the key breaking time is 168 sec, but the other methods taking 128 sec and 98 sec respectively. In addition, the execution time for the proposed and existing methods are analyzed and tabulated in table 3. From table 3 the proposed method reached better outcomes when compared with the other existing methods.

Table 1: Hacking percentage (%) MIM File Size in (kb) Proposed SVM ANN 10 8.23 10.12 11.63 20 8.63 10.46 12.08 30 9.12 11.36 13.47 40 7.28 11.27 13.55

Table 2: Key breaking Time Iteration Proposed SVM ANN 10 168 128 98 20 192 164 134 30 256 193 157 40 256 206 195

Table 3: Execution Time for various methods Iteration Proposed SVM ANN 10 2212 3157 3218 20 3215 4268 4318 30 4125 5534 5768 40 5234 6187 6348

0.6

0.5

0.4

0.3

0.2

0.1

0 Proposed AES-ASA RSA Blowfish

Fig.8: Comparison of Time taken (in seconds) by proposed ASA with that of conventional Techniques

Fig.7 shows the ROC curve plotted against the TPR and FPR for the proposed technique, SVM, RNN-LSTM, BPNN and RNN. The TPR rate is higher for the planned technique than that of the other techniques. Likewise, the FPR rate is lower for the projected technique than that of the other techniques. It implies that the proposed techniques perform well and it proves its efficiency in detecting the corrupted 3201 ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 data. The above Fig.8 shows the time taken by the encryption techniques such as proposed AES-ASA, conventional RSA and Blowfish. In the proposed technique we have combined AES and ASA for the secure encryption process. When compared these techniques, the RSA technique takes more time whereas the proposed technique takes less time. From the simulation outcomes, we can realize that the proposed system provides improved performance in both classification and encryption. Hence it can be used for real time purpose.

5. CONCLUSION In this study, a hybrid technique is proposed for the enhancement of security on Cc, which is constructed by combining RNN with ASA. Initially the incoming data comes from users are subjected to intruder detection. To detect whether intruder present in the data, the artificial intelligence based RNN classifier is utilized. After filtering the intruders from the data, the non-corrupted data is subjected to encryption process to increase security while transferring the data. AES-ASA based encryption is utilized in the process of encryption. After encryption, the secured data is transferred over the cloud. Here, the Man in middle attack is analyzed and the corresponding hacking percentage, key breaking time and execution is also determined. It shows that, the proposed method achieves improved results when compared the existing methods such as SVM and ANN. With the help of proposed method, the statistical measures are evaluated such as Accuracy, Recall, Precision and F-measure respectively. For the existing methods also the above measures are analyzed and compared with the proposed method.

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First Author:

International Journal of Advanced Science and Technology Vol. 29, No. 3, (2020), pp. 3191- 3204 Dr.K.Loheswaran is working as an Associate Professor in Department of CSE in CMR College of Engineering & Technology. He was completed his Ph.D in the year 2018, M.E CSE in the year 2010, B.E CSE in the year 2005. He was published 15 research papers in International Journals, National journals and IEEE conferences. He was conducted two National Conferences for the benefit of outside faculty and students. He has 14 years of Teaching Experience. His areas of interests are Cloud Computing, Machine learning, and Network Security.

Second Author: Dr.D.Murali is working as an Associate Professor in Department of CSE in CMR College of Engineering & Technology. He was received Ph.D (CSE), M.Tech (CS).B.Tech (CSE) from JNT University College of Engineering Kukatpally, Hyderabad, Telangana. His research interest includes Big Data analytics, Cloud Computing, and Machine Learning. He has been published 18 national and International journals. He attended 13 national and international conferences and published two books in the field of Software Engineering new approach and Database Management Systems.

Third Author:

D.Kalaiabirami is working as an Assistant Professor in Department of CSE in Vivekanandha College of Engineering for Women. She was completed her M.E CSE in the year 2013, B.E CSE in the year 2008.She was published 8 research papers in International Journals, National journals and conferences. She was conducted one National Conferences for the benefit of outside faculty and students. She has 11 years of Teaching Experience. Her areas of interests are Data Mining, Cloud Computing, Machine learning, and Network Security.