International Journal of Systems Assurance Engineering and Management 

About: International Journal of Systems Assurance Engineering and Management is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Computer science & Fuzzy logic. It has an ISSN identifier of 0976-4348. Over the lifetime, 1694 publications have been published receiving 13340 citations.

Cross-Site Scripting (XSS) attacks and defense mechanisms: classification and state-of-the-art

Abstract: Nowadays, web applications are becoming one of the standard platforms for representing data and service releases over the World Wide Web. Since web applications are progressively more utilized for security-critical services, therefore they have turned out to be a well-liked and precious target for the web-related vulnerabilities. Even though several defensive mechanisms have been building up to reinforce the modern web applications and alleviate the attacks instigated against them. We have analyzed the major concerns for web applications and Internet-based services which are persistent in several web applications of diverse organizations like banking, health care, financial service, retail and so on by the referring the Website Security Statistics Report of White Hat Security. In this paper, we highlight some of the serious vulnerabilities found in the modern web applications and revealed various serious vulnerabilities. Cross-Site Scripting (XSS) attack is the top most vulnerability found in the today’s web applications which to be a plague for the modern web applications. XSS attacks permit an attacker to execute the malicious scripts on the victim’s web browser resulting in various side-effects such as data compromise, stealing of cookies, passwords, credit card numbers etc. We have also discussed a high level of taxonomy of XSS attacks and detailed incidences of these attacks on web applications. A detailed comprehensive analysis of the exploitation, detection and prevention mechanisms of XSS attacks has also been discussed. Based on explored strength and flaws of these mechanisms, we have discussed some further work.

Maintenance optimization models and criteria

Abstract: Due to widespread automation and the high capital tied up in production equipment, the importance of maintenance is ever increasing. This makes maintenance an investment opportunity to be optimized, not a cost to be minimized. Academics have recognized this and many maintenance optimization models have been published over the years. Most of these models focus on one optimization criterion or objective, making multi-objective optimization models an underexplored area of maintenance optimization. Moreover, there is a big gap between academic models and application in practice. It is very difficult for industrial companies to adapt these models to their specific business context. This article reviews the literature on maintenance optimization models, with special focus on the optimization criteria and objectives used. To overcome flaws in present optimization models, a generic classification framework of maintenance optimization models is presented. All factors that have an influence on the optimization model will be made explicit and their links will be established. The framework is a starting point to develop business specific optimization models and enables decision making in e-maintenance. Moreover, it ensures a fit between the business model of a company and the maintenance optimization model right from the beginning. Future research will be on the development of a maintenance optimization model taking into account the most relevant optimization influence factors and criteria for a situation at hand.

A survey of machine-learning and nature-inspired based credit card fraud detection techniques

Abstract: Credit card is one of the popular modes of payment for electronic transactions in many developed and developing countries. Invention of credit cards has made online transactions seamless, easier, comfortable and convenient. However, it has also provided new fraud opportunities for criminals, and in turn, increased fraud rate. The global impact of credit card fraud is alarming, millions of US dollars have been lost by many companies and individuals. Furthermore, cybercriminals are innovating sophisticated techniques on a regular basis, hence, there is an urgent task to develop improved and dynamic techniques capable of adapting to rapidly evolving fraudulent patterns. Achieving this task is very challenging, primarily due to the dynamic nature of fraud and also due to lack of dataset for researchers. This paper presents a review of improved credit card fraud detection techniques. Precisely, this paper focused on recent Machine Learning based and Nature Inspired based credit card fraud detection techniques proposed in literature. This paper provides a picture of recent trend in credit card fraud detection. Moreover, this review outlines some limitations and contributions of existing credit card fraud detection techniques, it also provides necessary background information for researchers in this domain. Additionally, this review serves as a guide and stepping stone for financial institutions and individuals seeking for new and effective credit card fraud detection techniques.

Remaining useful life estimation: review

Abstract: This paper reviews the recent modelling developments in estimating the remaining useful life (RUL) of industrial systems. The RUL estimation models are categorized into experimental, data driven, physics based and hybrid approaches. The paper reviews some typical approaches and discusses their advantages and disadvantages. According to the literature, the selection of the best model depends on the level of accuracy and availability of data. In cases of quick estimations which are less accurate, the data driven method is preferred, while the physics based approach is applied when the accuracy of estimation is important.

An ISM approach for modelling the enablers in the implementation of Total Productive Maintenance (TPM)

Abstract: Total Productive maintenance (TPM) is increasingly implemented by many organizations to improve their equipment efficiency and to obtain the competitive advantage in the global market in terms of cost and quality. But, implementation of TPM is not an easy task. There are certain enablers, which help in the implementation of TPM. The utmost need is to analyse the behaviour of these enablers for their effective utilization in the implementation of TPM. The main objective of this paper is to understand the mutual interaction of these enablers and identify the ‘driving enablers’ (i.e. which influence the other enablers) and the ‘dependent enablers’ (i.e. which are influenced by others). In the present work, these enablers have been identified through the literature, their ranking is done by a questionnaire-based survey and interpretive structural modelling (ISM) approach has been utilized in analysing their mutual interaction. An ISM model has been prepared to identify some key enablers and their managerial implications in the implementation of TPM.