Security and usability are often thought of as being contradictive in nature. One affects the other negatively. The relation and trade-offs between usability and security must be detected during developing web application to satisfy the user's requirements with security perspective. Current approaches of usable-security emphasizes on building systems that are easy to use and secure as well. Hence, this paper is recognizing usability-security as a problem with different attributes contributing towards it. Further, there is a need to assess this problem for the satisfaction of the end user. In this context, this study proposes the track of Fuzzy AHP-TOPSIS (Analytic Hierarchy Process-Technique for Order of Preference by Similarity to Ideal Solution) technique to assess the usable-security of web application and also identifies the most prioritized attribute contributing towards building usable-security of web application. Moreover, to corroborate the efficacy of the proposed technique, the authors have tested the results on the institutional web applications. The results of the assessment undertaken in this study and the findings tabulated thereafter will be a helpful reckoner for the developers while designing web applications that afford optimum usable-security. INDEX TERMS Web application, usable-security, decision analysis, fuzzy TOPSIS, fuzzy AHP.
Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.
There has been a phenomenal increase in the use of web applications in every facet of human endeavor. From education, healthcare, banking, business to governance and so much more now depends on secure web applications. This accelerated growth in the use of web applications has led to increase in the complexity of security and hence the present day developers have to contribute more significantly towards meeting the users' requirements. However, the high security of web application is not yet efficacious enough because the durability of web application is not as much as it should be. In this context, it is important to consider that ensuring sustainability of security at the early stage of web application development process may reduce costs and rework entailed during the development of secure and durable web applications. Hence, there is a need to focus on increasing the lifespan of a secure web application. Quantitative estimation of security-durability plays a significant role for improving the lifespan of a secure web application. Thus, to optimize the security assurance effort for a specific lifespan , this paper is aimed at estimating the securitydurability of web application. For estimating security-durability, this paper uses a hybrid approach of Hesitant Fuzzy (HF) sets, Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) techniques. The effectiveness of the combined approach of HF-AHP-TOPSIS is tested for its accuracy in a web application for an academic institution, Babasaheb Bhimrao Ambedkar University in India. To check the sensitivity of outcomes, authors of the paper have taken altered forms of the University's web application. The result established contains the security-durability assessment. This work seeks to be an important contribution in enhancing the security-durability and would be beneficial for experts who are working in this domain.
This paper formulates the problem of building a context-aware predictive model based on user diverse behavioral activities with smartphones. In the area of machine learning and data science, a tree-like model as that of decision tree is considered as one of the most popular classification techniques, which can be used to build a data-driven predictive model. The traditional decision tree model typically creates a number of leaf nodes as decision nodes that represent contextspecific rigid decisions. However, in many practical scenarios within the context-aware environment, the generalized outcomes could play an important role to effectively capture user behavior. In this paper, we propose a behavioral decision tree, "BehavDT" context-aware model that takes into account user behavior-oriented generalization according to individual preference level. The BehavDT model outputs not only the generalized decisions but also the context-specific decisions in relevant exceptional cases. The effectiveness of our Be-havDT model is studied by conducting experiments on individual user real smartphone datasets. Our experimental results show that the proposed BehavDT contextaware model is more effective when compared with the traditional machine learning approaches, in predicting user diverse behaviors considering multi-dimensional contexts.
Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.
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