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Designing a Fuzzy-Logic Based Trust and Reputation Model for Secure Resource Allocation in Cloud Computing

Kamalanathan Chandran, Valarmathy Shanmugasudaram and Kirubakaran Subramani

Department  of Electronics and Communication Engineering, Bannari Amman

Institute of Technology, India

Abstract: To plan and improve a fuzzy logic and neural network based trust and reputation model for safe resource allocation in cloud computing is the most important motto of this research. Among the IT professionals in current scenario, the cloud computing is one of the main topics conversed. Now, to revise the security, we employ the trust manager and reputation manager in our proposed approach. At first, the user access a resource block through the scheduling manager and a structure will send to the user following accessing the resource block to fill the characteristic values of trust factor and reputation factor. The trust factor and reputation value is after that computed for the resource center and it is specified to the fuzzy logic system and neural network to obtain the security score of a resource center. To offer the security controls is the advantage of our suggested method in accessing the cloud resources from cloud computing owing to different security issues occurred in networks, databases, resource scheduling, transaction management and load balancing.

Keywords: Trust factor, reputation factor, fuzzy logic system, security score, resource center.

Received May 25, 2013; accepted June 19, 2013

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Efficient Transmission of PKI Certificates using ECC and its Variants

Shivkumar Selvakumaraswamy1, Umamaheswari Govindaswamy2

1Anna University, India

2PSG College of Technology, India

Abstract: The demand for wireless networks is increasing rapidly and it becomes essential to design existing Public-Key Infrastructure (PKI) useful for wireless devices. A PKI is a set of procedures needed to create, distribute and revoke digital certificates. PKI is an arrangement that binds public keys with respective user identities by means of a Certificate Authority (CA). The user identity must be unique within each CA domain. The third-party Validation Authority (VA) can provide this information on behalf of CA. The binding is established through the registration and issuance process which is carried out by software at a CA or under human supervision. Elliptic Curve Cryptography (ECC) is proved to be the best suited one for resource constrained applications. This paper compares the two PKI algorithms ECC and Rivest-Shamir-Adleman (RSA). It is found that ECC-based signatures on a certificate are smaller and faster to create; and the public key that the certificate holds is smaller as well. Verification is also faster using ECC-based certificates, especially at higher key strengths. The security of ECC systems is based on the elliptic curve discrete logarithm problem, rather than the integer factorization problem. This allows for faster computations and efficient transmission of certificates.

Keywords: ECC, PKI, wireless application protocol, registration authority, digital signature.

Received September 5, 2013; accepted December 24, 2013

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An Intelligent CRF Based Feature Selection for Effective Intrusion Detection

Sannasi Ganapathy1, Pandi Vijayakumar2, Palanichamy Yogesh1 and Arputharaj Kannan1
1Department of Information Science and Technology, Anna University, India
2Department of Computer Science and Engineering, University College of Engineering Tindivanam, India

Abstract: As the Internet applications are growing rapidly, the intrusions to the networking system are also becoming high. In such a scenario, it is necessary to provide security to the networks by means of effective intrusion detection and prevention methods. This can be achieved mainly by developing efficient intrusion detecting systems that use efficient algorithms which can identify the abnormal activities in the network traffic and protect the network resources from illegal penetrations by intruders. Though many intrusion detection systems have been proposed in the past, the existing network intrusion detections have limitations in terms of detection time and accuracy. To overcome these drawbacks, we propose a new intrusion detection system in this paper by developing a new intelligent Conditional Random Field (CRF) based feature selection algorithm to optimize the number of features. In addition, an existing layered approach based algorithm is used to perform classification with these reduced features. This intrusion detection system provides high accuracy and achieves efficiency in attack detection compared to the existing approaches. The major advantages of this proposed system are reduction in detection time, increase in classification accuracy and reduction in false alarm rates.

Keywords: Intrusion detection system, feature selection, false alarms, layered approach, intelligent CRF, ICRFFSA, LAICRF.

Received January 31, 2013; accepted November 10, 2013


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Using Ontologies for Extracting Differences in the Dynamic Domain: Application on Cancer Disease

Nora Taleb

Laboratory for Electronic Document Management LABGED Badji mokhtar University, Algeria


Abstract: Over time, the data representatives a given domain can change, both the data model reflecting the area. In this situation, the presence of strategies that can summarize the produced changes is mandatory. This study presents an implemented approach based on data mining techniques in order to extract the differences, the model is domain ontology and the changes are represented by two ontology’s versions. The results are summarized in changes report. An experimentation of the tool was made on the ontology of the cancer disease and satisfactory results were obtained.

 Keywords: Ontology change, ontology versionning, Web Ontology Language (OWL) scheme, retrieval information.

Received February 27, 2013; accepted September19, 2013


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wPFP-PCA: Weighted Parallel Fixed Point PCA Face Recognition

Chakchai So-In and Kanokmon Rujirakul
Department of Computer Science, Khon Kaen University, Thailand

Abstract: Principal Component Analysis (PCA) is one of the feature extraction techniques, commonly used in human facial recognition systems. PCA yields high accuracy rates when requiring lower dimensional vectors; however, the computation during covariance matrix and eigenvalue decomposition stages leads to a high degree of complexity that corresponds to the increase of datasets. Thus, this research proposes an enhancement to PCA that lowers the complexity by utilizing a Fixed Point (FP) algorithm during the eigenvalue decomposition stage. To mitigate the effect of image projection variability, an adaptive weight was also employed added to FP-PCA called wFP-PCA. To further improve the system, the advances in technology of multi-core architectures allows for a degree of parallelism to be investigated in order to utilize the benefits of matrix computation parallelization on both feature extraction and classification with weighted Euclidian Distance optimization. These stages include parallel pre-processor and their combinations, called weighed Parallel Fixed Point PCA wPFP-PCA. When compared to a traditional PCA and its derivatives which includes our first enhancement wFP-PCA, the performance of wPFP-PCA is very positive, especially in higher degree of recognition precisions, i.e., 100% accuracy over the other systems as well as the increase of computational speed-ups.

 Keywords: Face recognition, FP, parallel face recognition, parallel euclidian, parallel PCA, PCA.

 Received December 27, 2014; accepted May 21, 2014



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A General Characterization of Representing and Determining Fuzzy Spatial Relations

Luyi Bai1 and Li Yan2
1 College of Information Science and Engineering, Northeastern University, China
2 School of Software Northeastern University, China

 Abstract: A considerable amount of fuzzy spatial data emerged in various applications leads to investigation of fuzzy spatial data and their fuzzy relations. Because of complex requirements, it is challenging to propose a general fuzzy spatial relationship representation and a general algorithm for determining all fuzzy spatial relations. This paper,  presents a general characterization of representing fuzzy spatial relations assuming that fuzzy spatial regions are all fuzzy. On the basis of it, correspondences between fuzzy spatial relations and spatial relations are investigated. Finally, a general algorithm for determining all fuzzy spatial relations is proposed.

 Keywords: Fuzzy spatial data, fuzzy point, fuzzy line, fuzzy region, fuzzy spatial relations.

Received May 15, 2013; accepted March 17, 2013



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Empirical Evaluation of Syntactic and Semantic Defects Introduced by Refactoring Support

Wafa Basit1, Fakhar Lodhi2 and Usman Bhatti3
1Department of Computer Science, National University of Computer and Emerging Sciences, Pakistan
2Department of Computer Science, GIFT University, Pakistan
3Rmod Team, Inria Lille-Nord Europe, France 

Abstract: Software maintenance is a major source of expense in software projects. A proper evolution process is a critical ingredient in the cost-efficient development of high-quality software. A special case of software evolution is refactoring that cannot change the external behavior of the software system yet should improve the internal structure of the code. Hence, there is always a need to verify after refactoring, whether it preserved behavior or not. As formal approaches are hard to employ, unit tests are considered the only safety net available after refactoring. Refactoring may change the expected interface of the software therefore unit tests are also affected. The existing tools for refactoring do not adequately support unit test adaptation. Also, refactoring tools and guidelines may introduce semantic and syntactic errors in the code. This paper qualitatively and quantitatively analyses data from an empirical investigation involving 40 graduate students, performed against a set of semantic and syntactic defects. Findings from the expert survey on refactoring support have also been shared. The analysis in this paper shows that there are notable discrepancies between preferred and actual definitions of refactoring. However, continued research efforts are essential to provide Guide Lines(GL) in the adaptation of the refactoring process to take care of these discrepancies, thus improving the quality and efficiency of the software development.


Keywords: Refactoring, unit testing, pre-conditions, semantic defects, maintenance.


Received June 2, 2013; accepted March 29, 2013

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Adaptive Automata-based Model for Iterated n-Player’s Prisoner’s Dilemma

Sally Almanasra1, Khaled Suwais2 and Muhammad Rafie1

1School of Computer Sciences, Universiti Sains Malaysia, Malaysia

2Faculty of Computer Studies, Arab Open University, Saudi Arabia

 Abstract: In this paper, we present a new technique of representing the player’s strategies by adaptive automata, which can handle complex strategies in large populations effectively. The representation the player’s strategies have a great impact on changing the player’s behaviour in rational environments. This model is built on the basis of changing the behaviour of the player’s gradually toward the cooperation. The gradualism is achieved by constructing three different adaptive automata at three different levels. The results showed that our model could represent the player’s strategies efficiently. The results proofed that the model is able to enhance the cooperation level between the participated player’s through few tournaments.

Keywords: Adaptive automata, prisoner’s dilemma, cooperative behavior, INPPD.

Received October 3, 2013; accepted June 9, 2014

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Encryption Quality Measurement of a Proposed Cryptosystem Algorithm for the Colored Images Compared with Another Algorithm

Osama Abu Zaid1, Nawal El-Fishawy2 and Elsayed Nigm1

1Department of Mathematics, Zagazig University, Egypt

2 Department of Computer Science and Engineering, Menoufia University, Egypt

Abstract: In this paper, a proposed cryptosystem algorithm based on two different chaotic systems is presented. The  proposed cryptosystem algorithm is designated as PCACH. A recently developed encryption algorithm which is designated here as HuXia is reviewed. These two algorithms are applied to three images of different color frequencies, i.e., different types of colored-images are encrypted with each of the two encryption algorithms. Both of them are applied to the different images with two different types of encryption modes, Electronic Code Book (ECB) and Cipher Block Chaining (CBC). Visual inspection is not sufficient to assess the quality of encryption so other measuring factors are considered based on measuring the maximum deviation and the correlation coefficient between the original and the encrypted images. For judging the force of security, we measure the plain-text sensitivity by using NPCR and UACI analysis, measuring information entropy and measuring the key sensitivity. Also, the encryption/decryption time and the throughput are measured for the two algorithms. The results suggest that PCACH is a very good algorithm and superior to HuXia.

 Keywords: Encryption algorithms, image encryption, quality measurements, modes of encryption.

Received April 10, 2013; accepted June 23, 2013

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An Improved Clustering Algorithm for Text Mining: Multi-cluster Spherical K-means

Volkan Tunali1, Turgay Bilgin1 and Ali Camurcu2
1 Department of Software Engineering, Maltepe University, Turkey

2 Department of Computer Engineering, Fatih Sultan Mehmet Waqf University, Turkey

  Abstract: Thanks to advances in information and communication technologies, there is a prominent increase in the amount of information produced specifically in the form of text documents. In order to, effectively deal with this “information explosion” problem and utilize the huge amount of text databases, efficient and scalable tools and techniques are indispensable. In this study, text clustering which is one of the most important techniques of text mining that aims at extracting useful information by processing data in textual form is addressed. An improved variant of Spherical K-means algorithm named multi-cluster spherical K-means is developed for clustering high dimensional document collections with high performance and efficiency. Experiments were performed on several document data sets and it is shown that the new algorithm provides significant increase in clustering quality without causing considerable difference in CPU time usage when compared to Spherical K-means algorithm.

Keywords: Data mining, text mining, document clustering, spherical k-means.

Received February 10, 2013; accepted March 17, 2014

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VParC: A Compression Scheme for Numeric Data in Column-oriented Databases

Ke Yan1, Hong Zhu1 and Kevin Lü2

1School of Computer Science and Technology, Huazhong University of Science and Technology, China

2Brunel University, UK

Abstract: Compression is one of the most important techniques in data management, which is usually used to improve the query efficiency in database. However, there are some restrictions on existing compression algorithms that have been applied to numeric data in column-oriented databases. First, a compression algorithm is suitable only for columns with certain data distributions not for all kinds of data columns; second, a data column with irregular distribution is hard to be compressed; third, the data column compressed by using heavyweight methods cannot be operated before decompression which leads to inefficient query. Based on the fact that it is more possible for a column to have sub-regularity than have global-regularity, we developed a compression scheme called Vertically Partitioning Compression (VParC). This method is suitable for columns with different data distributions, even for irregular columns in some cases. The more important thing is that data compressed by VParC can be operated directly without decompression in advance. Details of the compression and query evaluation approaches are presented in this paper and the results of our experiments demonstrate the promising features of VParC.


Keywords: Column-stores, data management, compression, query processing, analytical workload.

Received August 28, 2013; accepted 21 April, 2014

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Data Mining Perspective: Prognosis of Life Style on Hypertension and Diabetes

Abdullah Aljumah and Mohammad Siddiqui
College of Computer Engineering and Sciences, Salman bin Abdulaziz University,
Kingdom of Saudi Arabia.

Abstract: In the present era, the data mining techniques are widely and deeply useful as decision support systems in the fields of health care systems. The proposed research is an interdisciplinary work of informatics and health care, with the help of data mining techniques to predict the relationship among interventions of hypertension and diabetes. As the study shows persons who have diabetes can have chances of hypertension and vice versa. In the present work we would like to approach the life style intervention of hypertension and diabetes and their effects using data mining. Life style intervention plays a vital role to control these diseases. The intervention includes the risk factor like diet, weight, smoking cessation and exercise. The regression technique is used in which dependent and independent variables are defined. The four interventions are treated as independent variables and two diseases hypertension and diabetes are dependent variables. We have established the relationship between hypertension and diabetes, using the data set of Non Communicable Disease NCD report of Saudi Arabia, World Health Organisation’s (WHO). The Oracle Data Miner (ODM) tool is used to analyse the data set. Predictive data analysis gives the result that interventions weight control and exercise have the direct relationship between them in both the diseases.

Keywords: Oracle data mining tool, prediction, regression, support vector machine, hypertension, diabetes.

Received April 10, 2014; accepted June 23, 2014

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Design and Implementation of a Synchronous and Asynchronous-Based Data Replication Technique in Cloud Computing

S.Kirubakaran, S. Valarmathy and C.Kamalanathan

Department of Electronics and Communication, Bannari Amman Institute of Technology, India

Abstract: Failures are usual rather exceptional in cloud computing environment. To access from the nearby site, the often used data should get replicated to multiple locations to compose the users to advance the system accessibility. A challenging task in cloud computing is to decide a sensible number and right location of replicas. At this point, we propose an adapted dynamic data replication approach to decide a sensible number and right location of replicas and we compare both the adapted dynamic data replication approach and normal dynamic data replication approach. The normal dynamic data replication approach has three dissimilar stages which are the recognition of data file to replicate, number of replicas to be created and placing new replicas. We adapt the popularity degree in the initial stage of normal dynamic data replication approach and also we consider the failure probability for replica factor calculation and the other two stages are related to the normal dynamic data replication approach. When we update the major data center we moreover integrate the synchronous and asynchronous updation of replica data file.


Keywords: Cloud computing, data replication, synchronous, asynchronous updation.


Received May 24, 2014; accepted December 7, 2014

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Enhancing the Optimal Robust Watermarking Algorithm to High Payload

1Satish Todmal and 2Suhas Patil 

1Department of Information Technology, JSPM’s Imperial College of

 Engineering and Research, India

2Department of Computer Engineering, Bharati Vidyapeeth Deemed University

College of Engineering, India


Abstract: Digital watermarking is a robust method that allows a person to attach hidden data into the digital audio, video, or image signals and documents. In this paper, we propose a watermarking technique where initially, the watermark is embedded into the HL and LH frequency coefficients in multi-wavelet transform domain after searching the optimal locations in order to improve both quality of watermarked image and robustness of the watermark. Here, the payload along with robustness is improved using Genetic Algorithm (GA) and multi-bit embedding procedure. The experimentation is carried using the different images and the performance of the technique is analyzed using the Perceptual Quality-Dependent Parameter (PSNR) and NC. The proposed technique is evaluated using various compression standards and filtering techniques which yielded good results by having high PSNR and NC values showing the robustness and fidelity of the technique.  The technique has achieved a peak PSNR of 38.14 and NC of 0.998. The technique is also compared to previous technique and results show that our proposed technique have performed better. Furthermore, payload analysis is carried out to infer that our proposed technique uses only half the payload when compared to previous technique.


Keywords: Watermarking, GA, optimal location, robustness, payload.


Received January 8, 2014; accepted July 8, 2014

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Neural Network with Bee Colony Optimization for MRI Brain Cancer Image Classification

Sathya Subramaniam and Manavalan Radhakrishnan

Department of Computer Science and applications, Periyar University, India

Abstract: Brain tumor is one of the foremost causes for the increase in mortality among children and adults. Computer visions are being used by doctors to analysis and diagnose the medical problems. Magnetic Resonance Imaging (MRI) is a medical imaging technique, which is used to visualize internal structures of MRI brain images for analyzing normal and abnormal prototypes of brain while diagnosing. It is a non-invasive method to take picture of brain and the surrounding images. Image processing techniques are used to extract meaningful information from medical images for the purpose of diagnosis and prognosis. Raw MRI brain images are not suitable for processing and analysis since noise and low contrast affect the quality of the MRI images. The classification of MRI brain images is emphasized in this paper for cancer diagnosis. It can consist of four steps: Pre-processing, identification of region of interest, feature extraction and classification. For improving quality of the image, partial differential equations method is proposed and its result is compared with other methods such as block analysis method, opening by reconstruction method and histogram equalization method using statistical parameters such as carrier signal to ratio, peak signal-to-ratio, structural similarity index measure, figure of merit, mean square error. The enhanced image is converted into bi-level image, which is utilized for sharpening the regions and filling the gaps in the binarized image using morphological operators. Region of Interest (ROI) is identified by applying region growing method for extorting the five features. The classification is performed based on the extracted image feature to determine whether the brain image is normal or abnormal and it is also, introduced hybridization of Neural Network (NN) with bee colony optimization for the classification and estimation of cancer affect on given MRI image. The performance of the proposed classifier is compared with traditional NN classifier using statistical measures such as sensitivity, specificity and accuracy. The experiment is conducted over 100 MRI brain images.

keywords: MRI images, NN, bee colony, PDE, biological analysis, feature extraction.


Received February 17, 2013; accepted October 24, 2014

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A DEA-Based Approach for Information Technology Risk Assessment through Risk IT Framework

Seyed Hatefi1 and Mehdi Fasanghari2

1Faculty of Engineering, Shahrekord University, Iran

2Cyber Space Research Institute, North Karegar St., Iran

Abstract: The use of Information Technology (IT) in organizations is subject to various kinds of potential risks. Risk management is a key component of project management enables an organization to accomplish its mission(s). However, IT projects have often been found to be complex and risky to implement in organizations. The organizational relevance and risk of IT projects make it important for organizations to focus on ways in order to successfully implement IT projects. This paper focuses on the IT risk management, especially the risk assessment model and proposes a process oriented approach to risk management. To do this end, this paper applies the risk IT framework which has three main domains, i.e., risk governance, risk analysis, risk response and 9 key processes. Then, a set of scenarios, which can improve the maturity level of risk IT processes, are considered and the impact of each scenario on the risk IT processes is determined by the expert opinions. Finally, the Data Envelopment Analysis (DEA) is customized to evaluate improvement scenarios and select the best one. The proposed methodology is applied to the Iran Telecommunication Research Centre (ITRC) to improve the maturity level of its IT risk management processes.

Keywords: Risk IT framework, risk management, process model, DEA.

Received June 10, 2012; accepted September 11, 2013

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