Global Journal of Computer Science and Technology (GJCST)
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Kerberos: Secure Single Sign-On Authentication Protocol Framework for Cloud Access Control
Get PDFCloud is a relatively new concept, so it is unsurprising that the security of information and data Protection concerns, network security and privacy still need to be addressed fully. The cloud allows clients to avoid hardware and software in Investments, gain flexibility, and cooperation with others, and to take advantage of sophisticated Services. However, security is a big problem for cloud clients especially access control; client profiles management and access services provided by public cloud environment. This article we are proposing an authentication model for cloud based on the Kerberos V5 protocol to provide single sign-on and to prevent against DDOS attacks in the access control system. This model could benefit by filtering against unauthorized access and to reduce the burden, computation and memory usage of cloud against authentication checks for each client. It acts as a trust third party between cloud servers and clients to allow secure access to cloud services. In this paper we will see some of the related work for cloud access control security issues and attacks. Then in next section we will discuss the proposed architecture
A Survey: Detection and Prevention of Wormhole Attack in Wireless Sensor Networks
Get PDFWireless Sensor Networks refers to a multi-hop packet based network that contains a set of mobile sensor nodes. Every node is free to travel separately on any route and can modify its links to other nodes. Therefore, the network is self organizing and adaptive networks which repeatedly changes its topology. The relations among nodes are restricted to their communication range, and teamwork with intermediate nodes is necessary for nodes to forward the packets to other sensor nodes beyond their communication range. The network2019;s broadcasting character and transmission medium help the attacker to interrupt network. An attacker can transform the routing protocol and interrupt the network operations through mechanisms such as selective forwarding, packet drops, and data fabrication. One of the serious routingdisruption attacks is Wormhole Attack. The main emphasis of this paper is to study wormhole attack, its detection method and the different techniques to prevent the network from these attack
Enhancement of Map Function Image Processing System using DHRF Algorithm on Big Data in the Private Cloud Tool
Get PDFCloud computing is the concept of distributing a work and also processing the same work over the internet. Cloud computing is called as service on demand. It is always available on the internet in Pay and Use mode. Processing of the Big Data takes more time to compute MRI and DICOM data. The processing of hard tasks like this can be solved by using the concept of MapReduce. MapReduce function is a concept of Map and Reduce functions. Map is the process of splitting or dividing data. Reduce function is the process of integrating the output of the Map2019;s input to produce the result. The Map function does two various image processing techniques to process the input data. Java Advanced Imaging (JAI) is introduced in the map function in this proposed work. The processed intermediate data of the Map function is sent to the Reduce function for the further process. The Dynamic Handover Reduce Function (DHRF) algorithm is introduced in the reduce function in this work. This algorithm is implemented in the Reduce function to reduce the waiting time while processing the intermediate data. The DHRF algorithm gives the final output by processing the Reduce function. The enhanced MapReduce concept and proposed optimized algorithm is made to work on Euca2ool (a Cloud tool) to produce an effective and better output when compared with the previous work in the field of Cloud Computing and Big Data
Mobile Cloud Computing
Get PDFCloud registering can be viewed as a model that gives system access to an imparted pool of assets, such as Storage and computing power, which can be rapidly provisioned and released with minimal management effort. This paper portrays an examination action in the region of portable cloud registering. It highlights diverse open issues which are related with the portable utilization of cloud processing. By making a list of criteria for those issues, diverse arrangements are compared against each other. the solutions discussed in this paper concentrate on diverse parts of cloud processing in relationship with portable utilization. Each of the exhibited arrangements offers atleast one satisfactory approach for one of the open issues that are related with the portable utilization of cloud processing assets. By consolidating the diverse existing approaches it would be possible to create an answer that covers most of the issues currently identified
Log-Gabor Orientation with Run-Length Code based Fingerprint Feature Extraction Approach
Get PDFThis paper aims to design and implement Log-Gabor filtering with Run-length Code based feature Extraction technique. Since minutiae extraction is an essential and core process of fingerprint Identification and Authentication systems, the minutiae features are enhanced in each orientation using Log-Gabor filter and features are extracted using the proposed method. Frequency domain is derived using FFT and they are enhanced by Log-Gabor filter for each orientation. In our method six orientations are considered; binarization, thinning are also followed. Fingerprint features are extracted using proposed method which possesses labeling and Run-length Coding technique. Our method is tested with the benchmark Databases and real time images and the results show the better performance and lower error rate
Mining Health Care Sequences using Weighted Associative Classifier
Get PDFThis paper proposes the general framework for mining sequences from health care database. The database is a relational model consisting of set of temporal records of individual patient consisting of basic information of the patient ie Patient_ID, age, gender etc. the second part is a series of sequences representing the set of treatment given to the patient during regular visit to the doctor and the third part is class label. Similarity search of sequences is performed to convert the database of sequences, to the database of items, so that apriori algorithm can be applied. Weighted association rule mining has been performed to find the frequent sequence of treatment provided to the patient. Classification association rules (CAR) having positive class label as consequent, represents the frequent sequence of treatment given to the patient for successful treatment. With the experimental results, author feels confident in declaring that the framework is feasible in the medical domain
Analysis of Distance Measures in Content based Image Retrieval
Get PDFContent predicated image retrieval (CBIR) provides an efficacious way to probe the images from the databases. The feature extraction and homogeneous attribute measures are the two key parameters for retrieval performance. A homogeneous attribute measure plays a paramount role in image retrieval. This paper compares six different distance metrics such as Euclidean, Manhattan, Canberra, Bray-Curtis, Square chord, Square chi-squared distances to find the best kindred attribute measure for image retrieval. Utilizing pyramid structured wavelet decomposition, energy levels are calculated. These energy levels are compared by calculating distance between query image and database images utilizing above mentioned seven different kindred attribute metrics. A sizably voluminous image database from Brodatz album is utilized for retrieval purport. Experimental results shows the preponderating of Canberra, Bray-Curtis, Square chord, and Square Chi-squared distances over the conventional Euclidean and Manhattan distances
Worker Productivity: A Fuzzy Supervised Neural Training Algorithm Approach
Get PDFProductivity refers to the physical relation between the quality produced (output) and the quantity of resource used in the course of production (input). Productivity is a relative term indicating the ratio between total output and the total inputs used therein on the other hand production is an absolute concept, which refers to the volume of output. Fuzzy Supervised Neural Network Training Algorithm has been designed and implemented with Matrix Laboratory (MATLAB) and Hypertext Preprocessor as the simulation language. This paper demonstrates the practical application of soft computing algorithm techniques in various well-meaning organizations
Selecting Optimal RBF Kernel with Machine Learning for Feature Extraction and Classification in SAR Images
Get PDFKernel methods are gaining popularity in image processing applications. The accuracy of feature extraction and classification on image data for a given application is greatly influenced by the choice of kernel function and its associated parameters. As on today there existing no formal methods for selecting the kernel parameters. The objective of the paper is to apply machine learning techniques to arrive at suitable kernel parameters and improvise the accuracy of kernel based object classification problem. The graph cut method with Radial Basis function (RBF) is employed for image segmentation, by energy minimization technique. The region parameters are extracted and applied to machine learning algorithm along with RBF2019;s parameters. The region is classified to be man made or natural by the algorithm. Upon each iteration using supervised learning method the kernel parameters are adjusted to improve accuracy of classification. Simulation results based on Matlab are verified for Manmade classification for different sets of Synthetic Aperture RADAR (SAR) Images
An Improved Apriori Algorithm based on Matrix Data Structure
Get PDFMining regular/frequent itemsets is very important concept in association rule mining which shows association among the variables in huge database. the classical algorithm used for extracting regular itemsets faces two fatal deficiencies .firstly it scans the database multiple times and secondly it generates large number of irregular itemsets hence increases spatial and temporal complexties and overall decreases the efficiency of classical apriori algorithm.to overcome the limitations of classical algorithm we proposed an improved algorithm in this paper with a aim of minimizing the temporal and spatial complexities by cutting off the database scans to one by generating compressed data structure bit matrix(b_matrix)-and by reducing redundant computations for extracting regular itemsets using top down method. theoritical analysis and experimental results shows that improved algorithm is better than classical apriori algorithm