No abstract

Over the years, electronic mail (e-mail) has been the target of several malicious attacks. Phishing is one of the most recognizable forms of manipulation aimed at e-mail users and usually, employs social engineering to trick innocent users into supplying sensitive information into an imposter website. Attacks from phishing emails can result in the exposure of confidential information, financial loss, data misuse, and others. This paper presents the implementation of a maximum entropy (ME) classification method for an efficient approach to the identification of phishing emails. Our result showed that maximum entropy with parsimonious feature space gives a better classification precision than both the Naïve Bayes and support vector machine (SVM).

Iris recognition algorithms have been proposed in several works with some of these algorithms solving mainly templates identification accuracy issues. The need to test these algorithms for identification or matching speed cannot be over-emphasized as this is also important when deploying algorithms in real application. This aim of this paper is to implement and validate a selected iris recognition algorithm. Performance evaluation was performed with the sole purpose of verifying the literature reported accuracy for the selected algorithm as well as to compute its identification speed on two databases (CASIA and BuIris) containing 600 iris images each. Results obtained matched the earlier 0% false acceptance with CASIA database but 42.3% with BuIris. This paper results verifies the scope of this algorithm and the need for improvement that could increase its adoptability globally.