Purpose Phishing is one of the major threats affecting businesses worldwide in current times. Organizations and customers face the hazards arising out of phishing attacks because of anonymous access to vulnerable details. Such attacks often result in substantial financial losses. Thus, there is a need for effective intrusion detection techniques to identify and possibly nullify the effects of phishing. Classifying phishing and non-phishing web content is a critical task in information security protocols, and full-proof mechanisms have yet to be implemented in practice. The purpose of the current study is to present an ensemble machine learning model for classifying phishing websites. Design/methodology/approach A publicly available data set comprising 10,068 instances of phishing and legitimate websites was used to build the classifier model. Feature extraction was performed by deploying a group of methods, and relevant features extracted were used for building the model. A twofold ensemble learner was developed by integrating results from random forest (RF) classifier, fed into a feedforward neural network (NN). Performance of the ensemble classifier was validated using k-fold cross-validation. The twofold ensemble learner was implemented as a user-friendly, interactive decision support system for classifying websites as phishing or legitimate ones. Findings Experimental simulations were performed to access and compare the performance of the ensemble classifiers. The statistical tests estimated that RF_NN model gave superior performance with an accuracy of 93.41 per cent and minimal mean squared error of 0.000026. Research limitations/implications The research data set used in this study is publically available and easy to analyze. Comparative analysis with other real-time data sets of recent origin must be performed to ensure generalization of the model against various security breaches. Different variants of phishing threats must be detected rather than focusing particularly toward phishing website detection. Originality/value The twofold ensemble model is not applied for classification of phishing websites in any previous studies as per the knowledge of authors.

Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential to ascertain the risk of bankruptcy at initial stages to prevent financial losses. In this perspective, different soft computing techniques can be employed to ascertain bankruptcy. This study proposes a bankruptcy prediction system to categorize the companies based on extent of risk. The prediction system acts as a decision support tool for detection of bankruptcy

No abstract

Advancement of unparalleled data in bioinformatics over the years is a major concern for storage and management. Such massive data must be handled efficiently to disseminate knowledge. Computational advancements in information technology present feasible analytical solutions to process such data. In this context, the paper is an attempt to highlight the influence of big data in bioinformatics. Some of the concepts emphasised are definition of big data; architectural platforms supporting data analytics; followed by the application of above-mentioned analytical techniques towards complex problems in bioinformatics. The challenges and future prospects of big data analytics in bioinformatics are briefly discussed. This paper provides a comprehensive summary of several data analytical techniques available for bioinformatics researchers and computer scientists.

No abstract