Phishing is the easiest way to use cybercrime with the aim of enticing people to give accurate information such as account IDs, bank details, and passwords. This type of cyberattack is usually triggered by emails, instant messages, or phone calls. The existing anti-phishing techniques are mainly based on source code features, which require to scrape the content of web pages, and on third-party services which retard the classification process of phishing URLs. Although the machine learning techniques have lately been used to detect phishing, they require essential manual feature engineering and are not an expert at detecting emerging phishing offenses. Due to the recent rapid development of deep learning techniques, many deep learning-based methods have also been introduced to enhance the classification performance. In this paper, a fast deep learning-based solution model, which uses character-level convolutional neural network (CNN) for phishing detection based on the URL of the website, is proposed. The proposed model does not require the retrieval of target website content or the use of any third-party services. It captures information and sequential patterns of URL strings without requiring a prior knowledge about phishing, and then uses the sequential pattern features for fast classification of the actual URL. For evaluations, comparisons are provided between different traditional machine learning models and deep learning models using various feature sets such as hand-crafted, character embedding, character level TF-IDF, and character level count vectors features. According to the experiments, the proposed model achieved an accuracy of 95.02% on our dataset and an accuracy of 98.58%, 95.46%, and 95.22% on benchmark datasets which outperform the existing phishing URL models.

During the last decade, information and communication technology has brought remarkable changes to the education style of developed countries, especially in the context of online learning materials accessibility. However, in developing nations such as the East African (EA) countries, university students may lack the necessary ICT training to take advantage of e-learning resources productively. Therefore, the comprehension of the key factors behind ICT fluency is a significant concern for this region and all the developing countries in general. This paper applies the Concentration Index and proposes a Logistic Regression based model to discover the key determinants of ICT fluency and to explore the evolution of the digital divide among EA students within the four years of undergraduate studies. To identify the principal determinants, data composing of 1237 participants is collected from three different universities in EA within a one year period. The experimental results indicate that the digital divide among students decreases quite fast from the first year to the fourth year. Regression computational findings show that the key determinants of ICT fluency are the student urban/rural origin, computer ownership, computer experience, class year, and major. The findings provide heuristic implications for developers, practitioners, and policy makers for an improved ICT environment implementation in EA and the developing nations in general.

Information and communication technology (ICT) has reshaped the traditional education system into a flexible and efficient e-learning context. However, computer usage, attitudes, and some other critical factors are the primary concern to implement e-learning in developing countries. This paper applies computer attitude scale and proposes a prediction model based on multiple linear regression to discover the key factors affecting computer attitudes for the implementation of the ICT-based learning environment. To identify the principal factors, we collect the data sets by the active participation of more than 900 undergraduate students from three different universities of East African Community countries in one year period. The experimental results reveal that the students are using technology in their daily lives, however, they are not using it to support their daily study. Qualitative evidence indicates that the university has lower requirements for using computers, and the restrictive rules to use technologies. In addition, the universities are in a shortage of necessary computer hardware and software. Besides, the factors which are affecting the attitudes toward computers are the urban or rural origin of the student, English language level, and parental encouragement.