Volume 3, Issue 9, September 2011

Privacy and Security in Distributed Systems Using Three-Factor Authentication

A. Rengarajan, C. Jayakumar and R. Sugumar

As part of the security within distributed systems, various services and resources need protection from unauthorized use. Remote authentication is the most commonly used method to determine the identity of a remote client. This paper investigates a systematic approach for authenticating clients by three factors, namely password, smart card, and biometrics. A generic and secure framework is proposed to upgrade two-factor authentication to three-factor authentication. The conversion not only significantly improves the information assurance at low cost but also protects client privacy in distributed systems. In addition, our framework retains several practice-friendly properties of the underlying two-factor authentication, which we believe is of independent interest.

Hamiltonian Paths in K-alphabet Grid Graphs

Fatemeh Keshavarz-Kohjerdi and Alireza Bagheri

Given a graph G, the Hamiltonian path problem is the problem of deciding whether a graph G contains a simple path that visits each vertex of G exactly once. This problem is a NP-complete problem for general grid graphs. In this paper, we provide the necessary and sufficient condition for the Hamiltonian path problem on special class of grid graphs, namely on a K-alphabet grid graphs.

Intelligent Agent Oriented Traffic Control Architecture

Sadia Afsar, Abdul Mateen and Fahim Arif

Computers has been evolved from the single user, static and dumb system to open, distributed dynamic and intelligent system. The evolved systems are working in distributed fashion, share information with other systems and improve their behavior through previous experience or statistics. The agent-based systems date back to the 90’s and is increasingly becoming an interesting and important area of research. Many computer researchers and programmers believe that agent oriented has provided the new and most important direction in software engineering and all other related fields. Our research proposed an intelligent vehicle traffic management system that uses autonomous agents. The proposed system uses the knowledge about the flow of previous signal to predict the incoming flow of current signal as well as the knowledge about the rescue vehicles. The architecture involves the video analysis and exploration using machine learning techniques to estimate and guess the flow of the vehicle traffic.

Design and Implementation of Echo State Network for Intrusion Detection

S. Saravanakumar, Jaya Kumar and S. Purushothaman

The complexity and number of attacks on computer systems are increasing day-by-day. Intrusion detection is a process of monitoring the various computer networks and systems for violations of security and this can be automatically done with the help of an intrusion detection system. An Intrusion Detection System (IDS) is a critical component for secure information management. IDS play a major role in detecting and disrupting various attacks before cooperating with the software. This work presents the investigations carried out on Echo State Network (ESN) structures for intrusion detection. New algorithms have been presented which have faster convergence and better performance in IDS from a set of available information in the database. This paper has been implemented with the KDD dataset to experiment the performance of ESN in classifying the Local Area Network (LAN) intrusion packets.

A Hybrid Despeckling Model for Medical Ultrasound Images

R.Vanithamani, G.Umamaheswari, A.AjayKrishnan, C.Ilaiyarasan, K. Iswariya and C.G.Kritika

A hybrid despecklingmodelbased on wavelet shrinkage and bilateral filter isdesigned and tested for ultrasound images. The different wavelet thresholding techniques along with bilateral filter before decomposition and after reconstruction using wavelets are compared against NeighShrinkSURE technique with and without bilateral filter.The performance of the proposed method is assessed using standard performance metrics like Signal to Noise Ratio (SNR), Edge Preservation Index (EPI) and Mean Square Error(MSE). The results demonstrate that the use of bilateral filter in combination with NeighShrinkSUREthresholding technique yields an increase in the Signal to Noise Ratio(SNR), Edge Preservation Index (EPI) anda decrease in the Mean Square Error (MSE) when compared to other methods.

Reusability metrics - An Evolution based Study on Object Oriented System, Component based System and Service Oriented System

G. Shanmugasundaram, V. Prasanna Venkatesan and C. Punitha Devi

Reusability is the key paradigm for increasing software quality in the software development. Reusability was the core concept of object oriented systems which there on evolved into component based systems and service oriented systems. The aim of this paper is to study the reuse metrics of these three systems and to propose a model to bring out the relationship. A template has been designed to study and record how the metrics are categorised and it forms the base for the evolution based model. The outcome of the study was brought out by an evolutionary based model which states the maturity level of reuse metrics and identifies the gaps to measure complete reusability for service oriented systems.

Performance Comparison of Daubechies Wavelet Family for Bangla Vowel Synthesis

Shahina Haque and Tomio Takara

Bangla phoneme synthesis using conventional methods is primarily based on inspection of Fourier Transform (FT) which has resolution problem. In order to produce better accuracy, we attempted Wavelet Transform (WT) with Daubechies wavelet family for analyzing and synthesizing the seven Bangla vowels. The performance of the synthesized speech signal is measured by four methods. It is observed from our study that Daubechies12(db12) wavelet at decomposition level 5, stores more than 97% of the energy in the first few approximation coefficient with highest SNR, PSNR and reproduces the signal with lowest NRMSE.

A Hybrid method for Iris Segmentation

Muaadh Sh. Azzubeiry and Dzulkifli Bin Mohammed

Advance development in security technology has caused many major corporations and governments to start employing modern techniques in identifying the identity of the individuals. Among the common biometric identification methods are facial recognition, fingerprint recognition, speaker verification and so on, presenting a new solution for applications that require a high degree of security. Among these biometric methods, iris recognition becomes an important topic in pattern recognition and it depends on the iris which is located in a place that is still stable through human life. Furthermore, the probability to find two identical irises approaching to zero value is quite easy. The identification system consists of several stages, and segmentation is the most crucial step. The current segmentation methods still have its limitation in localizing the iris due to the circular shape consideration of the pupil. In this paper an enhanced hybrid method, which can guarantee the accuracy of the iris identification system is proposed. The proposed method takes into account the elliptical shape of the pupil and iris. Moreover, Eyelid detection is another step that has been proposed in this paper as a part of segmentation stage. The dataset which is used is CASIA v3 including the three subsets: Interval, Lamp and Twin. The performance measurement of the proposed method is done by determining the number of success images. The results of the study are very promising with an accuracy of 99.1% as compared to the related existing methods.

Performance Analysis of Routing and Wavelength Assignment Algorithms for Hybrid Multicast Networks

D. V. Pradeep Sankar and K. Muneeswaran

Most of the existing Routing and Wavelength Assignment algorithms consider only the routing and available wavelengths for unicast traffic, but those works not considering the multicast traffic that may be important constraint in many real-time multicast applications. In this paper, we consider the problem of routing connections with differentiated time constraints in wavelength-division multiplexing mesh networks. For this purpose, we propose the modified greedy techniques to reduce the call blocking. Experiments have been conducted using our Multicast Routing and Wavelength Assignment algorithm by constructing multiple spanning trees using Mayeda and Seshu algorithm and the call blocking probabilities for various network structures are reported. The performance results of the proposed method shows promising outcome of our algorithm.

A Partitioning-based method according to budget distribution for task scheduling in Computational Grids

Mostafa Ghobaei Arani, Sam Jabbehdari and Nasser Modiri

The goal of computational grids is to aggregate heterogeneous distributed resources for solving large-scale problems in science, engineering and commerce. Unfortunately dynamism and heterogeneity of grid resources and also various demands for applications on grids cause the complexity of grid scheduling. So for having access to high performance in grid systems, It is necessary to get effective scheduling for resources. Most Quality of Service (QoS) constraint based workflow scheduling algorithms are based on either budget or deadline constraints. In this paper, we solve the problem of budget constraint-based scheduling through dividing total problem on several partitions and budget distribution on each of them. After budget distribution, we can find a local optimal schedule for each partition based on its sub-budget. We evaluate proposed algorithm compared with Back-tracking and BTO scheduling algorithms in the fields of time and cost execution. Simulation experimental results shows that proposed algorithm provide better performance in low-level budgets.

A Hybrid Model Based on Feature Extraction for Network Intrusion Detection

Saeed Khazaee and Mohammad Saniee Abadeh

Intrusion Detection is one of the most important ways to increase computer network security. In this paper, we intend to explore the feasibility of applying feature extraction methods to the misuse detection task. The feature extraction stage is performed by several classifiers which we call FE-Clasifiers. In this study, instead of the usual five classes, to improvement of recognition accuracy, some subclasses are intended. So, here we have 11 classes not 5 classes. Evaluation of the proposed method is performed by KDDCup99 data set. Our experimental results indicate that intrusion detection system with feature extraction method has better performance than that without feature extraction method in classification rate, detection rate and false alarm rate.

Early Usability Model for Object-Oriented System

Sanjay Kumar Dubey, Arashdeep Kaur and Ajay Rana

Usability is an important quality factor which is included in most of the quality models due to its consequences. The requirement of quality software causes the development of new techniques which can be used in developing model for predicting usability of software systems. One such technique is clustering. In this paper various metrics dataset are being used to assess the usability, namely, CM1, PC1 and JM1 taken from a public NASA data set. The metric sets include requirement time metrics and module metrics. These metrics are then combined using inner join method. This paper assess the usability of software system to be developed, at early stages of software life cycle using software fault data available from already developed similar kind of projects. Hence, examines the relationship between usability and software fault using K-Means clustering.

Implementation of Decision Support System for Outdoor Sports Using Machine Learning Techniques

Noreen Akram, Asim Munir, Memoona Khanam and Malik Sikander Hayat Khiyal

This paper presents a Decision Support System for Outdoor Sports. Outdoor Sports are greatly affected by the weather condition so basically the support provided by the system proposed is the decision whether to carry out the game or not based on the weather and field conditions. The weather attributes taken into consideration are Outlook, Temperature, Humidity, and Wind. The Field values taken are hard surface, Grass and Clay. The game considered is Tennis. The system is implemented using Machine Learning technique Decision Tree Learning, The algorithm selected is ID3 algorithm. The tool used is MATLAB for the frontend and MS Access for the backend. The algorithm generate decision tree. Rules are formulated from the decision tree. The system makes decision based on these rules. The system is tested using 25 sample records. The average accuracy of the system comes out to be 84%. This system can be extended for other games.

A Survey of University Courses Timetable Scheduling Problem

Addin Osman, Adlan Balola, Anwar Ali Yahya and Yahya Ali Abdelrahman

This paper presents the mathematical definition of university courses timetable scheduling problem. The paper only shows the courses timetable. The genetic algorithm has been defined and its implementation for solving university courses timetable scheduling problem has been presented. A summary of some works related to the implementation of genetic algorithms in university courses timetable scheduling problems are presented.