Browsing by Author "Chiroma, Haruna"

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    Deep Learning Architectures in Emerging Cloud Computing Architectures: Recent Development, Challenges and Next Research Trend
    (Applied Soft Computing, Elsevier, 2020) Jauro, Fatsuma; Chiroma, Haruna; Gita, Abdulsalam Y.
    The challenges of the conventional cloud computing paradigms motivated the emergence of the next generation cloud computing architectures. The emerging cloud computing architectures generate voluminous amount of data that are beyond the capability of the shallow intelligent algorithms to process. Deep learning algorithms, with their ability to process large-scale datasets, have recently started gaining tremendous attentions in the emerging cloud computing literatures. However, no comprehensive literature review exists on the applications of deep learning approaches to solve complex problems in emerging cloud computing architectures. To fill this gap, we conducted a comprehensive literature survey on deep learning in emerging cloud computing architectures. The survey shows that deep learning algorithms in emerging cloud computing architectures are increasingly becoming an interesting research area for solving complex problems. We introduce a new taxonomy of deep learning techniques for emerging cloud computing architectures and provide deep insights into the current state-of-the-art active research works on deep learning to solve complex problems in emerging cloud computing architectures. The synthesis and analysis of the articles as well as their limitation are presented. A lot of challenges were identified in the literature and new future research directions to solve the identified challenges are presented. We believed that this article can serve as a reference guide to new researchers and an update for expert researchers to explore and develop more deep learning applications in the emerging cloud computing architectures
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    Machine learning algorithms for improving security on touch screen devices: a survey, challenges and new perspectives
    (Neural Computing and Applications, 2020) Bello, Auwal Ahmed; Chiroma, Haruna; Gital, Abdulsalam Ya’u
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    A maximum entropy classification scheme for phishing detection using parsimonious features
    (TELKOMNIKA Telecommunication, Computing, Electronics and Control, 2021-06-13) Bello, Auwal Ahmed; Chiroma, Haruna; Gital, Abdulsalam Ya’u
    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)