Arunava Roy

Toward the design of adaptive selection strategies for multi-factor authentication

Define authentication factors.Evaluate trustworthy values of different authentication factors.Evaluate trustworthy values of different sets of authentication factors.Design multi-objective optimization strategies for adaptive multi-factor authentication.Conducting experiments for checking the efficiency and effectiveness of the proposed approach. Authentication is the fundamental safeguard against any illegitimate access to a computing device and other sensitive online applications. Because of recent security threats, authentication through a single factor is not reliable to provide adequate protection of these devices and applications. Hence, to facilitate continuous protection of computing devices and other critical online services from unauthorized access, multi-factor authentication can provide a viable option. Many authentication mechanisms with varying degrees of accuracy and portability are available for different types of computing devices. As a consequence, several existing and well-known multi-factor authentication strategies have already been utilized to enhance the security of various applications. Keeping this in mind, we developed a framework for authenticating a user efficiently through a subset of available authentication modalities along with their several features (authentication factors) in a time-varying operating environment (devices, media, and surrounding conditions, like light, noise, motion, etc.) on a regular basis. The present work is divided into two parts, namely, a formulation for calculating trustworthy values of different authentication factors and then the development of a novel adaptive strategy for selecting different available authentication factors based on their calculated trustworthy values, performance, selection of devices, media, and surroundings. Here, adaptive strategy ensures the incorporation of the existing environmental conditions on the selection of authentication factors and provides significant diversity in the selection process. Simulation results show the proposed selection approach performs better than other existing and widely used selection strategies, mainly, random and optimal cost selections in different settings of operating environments. The detailed implementation of the proposed multi-factor authentication strategy, along with performance evaluation and user study, has been accomplished to establish its superiority over the existing frameworks.

An Adaptive Approach Towards the Selection of Multi-Factor Authentication

Authentication is the fundamental defense against any illegitimate access to a computing device or any sensitive online applications. Due to recent trends of emerging security threats, authentication using only a single factor is not reliable to provide adequate protection for these devices and applications. Hence, to facilitate continuous protection of computing devices and other critical online services from an un-authorized access, multi-factor authentication emerges as a viable option. Many authentication mechanisms with varying degrees of accuracy and portability are available for different types of computing devices connected with various communicating media. As a consequence, several existing and well-known multi-factor authentication strategies have already been utilized to enhance the security of various applications. Keeping this in mind, this research is focused on designing a robust and scalable framework for authenticating a legitimate user efficiently through a subset of available authentication modalities along with their several features (authentication factors) in time-varying operating environments (devices, media and surrounding conditions) on a regular basis. This paper highlights the creation of a trustworthy framework to quantify different authentication factors in terms of selection of different types of devices and media. In addition, a novel adaptive selection strategy for the available authentication factors incorporating the trustworthy values, previous history of selection as well as surrounding conditions is proposed in the paper. Selection through adaptive strategy ensures the incorporation of the existing environmental conditions within the selection of authentication factors and provides better diversity in the selection of these factors. Simulation results show that the proposed selection approach performs better than other existing selection strategies, namely, random and optimal selections in different settings of operating environments.

Novel Algorithms for Web Software Fault Prediction

Reliability is gaining importance with time for Web system because of the popularity of different Web-based applications, namely, different Web sites and social community networks. In order to aggrandize the reliability of a Web system, some methods are required to measure its current reliability. In recent years, some research works have been carried out on Web software error analysis and reliability predictions. In all these research works, the Web environment has been considered as a crisp one. This is not in reality. Hence, in this paper, a novel clustering algorithm and a multivariate fuzzy logic and fuzzy time series based prediction algorithm for Web software fault prediction have been developed. The proposed prediction algorithm can predict the occurrences of more than one Web error, in a single day, on a single run. Proposed methods have been validated using some real Web failure data extracted from the HTTP logs (access and error logs) of www.ismdhanbad.ac.in (the official Web site of Indian School of Mines Dhanbad, India), which were collected from the Indian School of Mines Web server. Copyright

Web software fault prediction under fuzzy environment using MODULO-M multivariate overlapping fuzzy clustering algorithm and newly proposed revised prediction algorithm

In recent years some research works have been carried out on web software error analysis and reliability predictions. In all these works the web environment has been considered as crisp one, which is not a very realistic assumption. Moreover, web error forecasting remains unworthy for the researchers for quite a long time. Furthermore, among various well known forecasting techniques, fuzzy time series based methods are extensively used, though they are suffering from some serious drawbacks, viz., fixed sized intervals, using some fixed membership values (0, 0.5, and 1) and moreover, the defuzzification process only deals with the factor that is to be predicted. Prompted by these facts, the present authors have proposed a novel multivariate fuzzy forecasting algorithm that is able to remove all the aforementioned drawbacks as also can predict the future occurrences of different web failures (considering the web environment as fuzzy) with better predictive accuracy. Also, the complexity analysis of the proposed algorithm is done to unveil its better run time complexity. Moreover, the comparisons with the other existing frequently used forecasting algorithms were performed to demonstrate its better efficiency and predictive accuracy. Additionally, at the very end, the developed algorithm was applied on the real web failure data of http://www.ismdhanbad.ac.in/, the official website of ISM Dhanbad, India, collected from the corresponding HTTP log files.

A novel multivariate fuzzy time series based forecasting algorithm incorporating the effect of clustering on prediction

Forecasting has often played predominant roles in daily life as necessary measures can be taken to bypass the undesired and detrimental future prompted by this fact, the issue of forecasting becomes one of the most important topics of research for the modern scientists and as a result several innovative forecasting techniques have been developed. Amongst various well-known forecasting techniques, fuzzy time series-based methods are successfully used, though they are suffering from some serious drawbacks, viz., fixed sized intervals, using some fixed membership values (0, 0.5, and 1) and moreover, the defuzzification process only deals with the factor that is to be predicted. Additionally, most of the existing and widely used fuzzy time series-based forecasting algorithms employ their own clustering techniques that may be data-dependent and in turn the predictive accuracy decrease. Prompted by the fact, the present author developed a novel multivariate fuzzy forecasting algorithm that is able to remove all the drawbacks as also can predict the future occurrences with better predictive accuracy. Moreover, the comparisons with the thirteen other existing frequently used forecasting algorithms (viz., conventional, fuzzy time series-based algorithms and ANN) were performed to demonstrate its better efficiency and predictive accuracy. Towards the end, the applicability and predictive accuracy of the developed algorithm has been demonstrated using three different data sets collected from three different domains, such as: oil agglomeration process (coal washing technique), frequently occurred web error prediction and the financial forecasting. The real dataset related to oil agglomeration was collected from CIMFER, Dhanbad, India, that regarding the frequently occurred web error codes of www.ismdhanbad.ac.in, the official website of ISM Dhanbad, was collected from the Indian School of Mines (ISM) Dhanbad, India server and the finance data set was collected from the Ministry of Statistical and Program Implementation (Govt. of India).

Software fault prediction using neuro-fuzzy network and evolutionary learning approach

Abstract In the real world, a great deal of information is provided by human experts that normally do not conform to the rules of physics, but describe the complicated systems by a set of incomplete or vague statements. The need of conducting uncertainty analysis in software reliability for the large and complex system is demanding. For large complex systems made up of many components, the uncertainty of each individual parameter amplifies the uncertainty of the total system reliability. In this paper, to overcome with the problem of uncertainty in software development process and environment, a neuro-fuzzy modeling has been proposed for software fault prediction. The training of the proposed neuro-fuzzy model has been done with genetic algorithm and back-propagation learning algorithm. The proposed model has been validated using some real software failure data. The efficiency of the two learning algorithms has been compared with various fuzzy and statistical time series-based forecasting algorithms on the basis of their prediction ability.

A fuzzy decision support system for multifactor authentication

Multifactor authentication (MFA) is a growing trend for the accurate identification of the legitimate users through different modalities such as biometrics, nonbiometric, and cognitive behavior metric. In this paper, we have developed an adaptive MFA that considers the effects of different user devices, media, environments, and the frequency of authentication to detect the legitimate user. For this purpose, initially, we have evaluated the trustworthiness values of all the authentication modalities in different user devices and media using a nonlinear programming problem with probabilistic constraints. Finally, an evolutionary strategy, using fuzzy “IF–THEN” rule and genetic algorithm has been developed for the adaptive selection of authentication modalities. We have done a numerical simulation to prove the effectiveness and efficiency of the proposed method. Moreover, we have developed a prototype client–server-based application and have done a detailed user study to justify its better usability than the existing counterparts.

Toward the development of a conventional time series based web error forecasting framework

Web reliability is gaining importance with time due to the exponential increase in the popularity of different social community networks, mailing systems and other online applications. Hence, to enhance the reliability of any existing web system, the web administrators must have the knowledge of various web errors present in the system, influences of various workload characteristics on the manifestation of several web errors and the relations among different workload characteristics. But in reality, often it may not be possible to institute a generalized correspondence among several workload characteristics. Moreover, the issues like the prediction and estimation of the cumulative occurrences of the source content failures and the corresponding time between failures of a web system become less highlighted by the reliability research community. Hence, in this work, the authors have presented a well-defined procedure (a forecasting framework) for the web admins to analyze and enhance the reliability of the web sites under their supervision. Initially, it takes the HTTP access and the error logs to extract all the necessary information related to the workloads, web errors and corresponding time between failures. Next, we have performed the principal component analysis, correlation analysis and the change point analysis to select the number of independent variables. Next, we have developed various time series based forecasting models for foretelling the cumulative occurrences of the source content failures and the corresponding time between failures. In the current work, the multivariate models also include various uncorrelated workloads, the exogeneous and the endogenous noises for forecasting the web errors and the corresponding time between failures. The proposed methodology has been validated with usage statistics collected from the web sites belong of two highly renowned Indian academic institutions.

Multi-Factor Authentication

Multi-Factor authentication (MFA) is a secure process of authentication which requires more than one authentication technique chosen from independent categories of credentials. Like single factor, multi-factor is increasingly used to verify the users’ identities in accessing the cyber system and information. MFA combines two or more types of authentication to provide better and secure way of authenticating users.

TRANSFER FUNCTION MODELING IN WEB SOFTWARE FAULT PREDICTION IMPLEMENTING PRE-WHITENING TECHNIQUE

The issue of Web reliability is gaining importance, as different Web-based applications are getting popularity with time. In order to enhance the reliability of a Web system, the Web administrator have to determine if there exists any relationship or correlation among different Web workload characteristics and the errors having an impact on the reliability of the Web system, so that he will be able to predict them accurately. It may not be possible to establish a generalized relationship among different Web workload characteristics. Hence, in this paper, we have performed principal component analysis (PCA) to check whether different Web workload characteristics, for particular Web software are correlated or not. Then, we have proposed a transfer function based model for Web software fault prediction. Also, we have used the pre-whitening technique to eliminate the noise present in the data for developing an efficient transfer function based model to predict the cumulative occurrences of different Web failures having an impact on the reliability of the Web software.