The phenomenon of phishing has now been a common threat, since many individuals and webpages have been observed to be attacked by phishers. The common purpose of phishing activities is to obtain user’s personal information for illegitimate usage. Considering the growing intensity of the issue, this study is aimed at developing a new hybrid rule‐based solution by incorporating six different algorithm models that may efficiently detect and control the phishing issue. The study incorporates 37 features extracted from six different methods including the black listed method, lexical and host method, content method, identity method, identity similarity method, visual similarity method, and behavioral method. Furthermore, comparative analysis was undertaken between different machine learning and deep learning models which includes CART (decision trees), SVM (support vector machines), or KNN (K‐nearest neighbors) and deep learning models such as MLP (multilayer perceptron) and CNN (convolutional neural networks). Findings of the study indicated that the method was effective in analysing the URL stress through different viewpoints, leading towards the validity of the model. However, the highest accuracy level was obtained for deep learning with the given values of 97.945 for the CNN model and 93.216 for the MLP model, respectively. The study therefore concludes that the new hybrid solution must be implemented at a practical level to reduce phishing activities, due to its high efficiency and accuracy.

The development of the Internet of Things will greatly facilitate the process of patient's diagnosis and monitoring, with small IP-based wireless sensors implemented on the patient's body, his physiological parameters, such as blood pressure and heart rate, can be monitored remotely and continuously. This scenario must absolutely respect the confidentiality and privacy of patient's medical information, only caregivers and authorized persons should possess the right to access these information. The security must be ensured throughout the healthcare application scenario. This paper presents a study of the potential security problems in this application, and proposes a security model as a solution. Our model is based on symmetric cryptography with a proposed key management system and network nodes authentication mechanism.

Automatic short answer grading (ASAG) has become part of natural language processing problems. Modern ASAG systems start with natural language preprocessing and end with grading. Researchers started experimenting with machine learning in the preprocessing stage and deep learning techniques in automatic grading for English. However, little research is available on automatic grading for Arabic. Datasets are important to ASAG, and limited datasets are available in Arabic. In this research, we have collected a set of questions, answers, and associated grades in Arabic. We have made this dataset publicly available. We have extended to Arabic the solutions used for English ASAG. We have tested how automatic grading works on answers in Arabic provided by schoolchildren in 6th grade in the context of serious games. We found out those schoolchildren providing answers that are 5.6 words long on average. On such answers, deep learning-based grading has achieved high accuracy even with limited training data. We have tested three different recurrent neural networks for grading. With a transformer, we have achieved an accuracy of 95.67%. ASAG for school children will help detect children with learning problems early. When detected early, teachers can solve learning problems easily. This is the main purpose of this research.

In this paper, we explore the feasibility of leveraging the accelerometer and gyroscope sensors in modern smartphones to detect instances of distracted driving activities (e.g., calling, texting, and reading while driving). To do so, we conducted an experiment with 16 subjects on a realistic driving simulator. As discussed later, the simulator is equipped with a realistic steering wheel, acceleration/braking pedals, and a wide screen to visualize background vehicular traffic. It is also programmed to simulate multiple environmental conditions like daytime, nighttime, fog, and rain/snow. The subjects were instructed to drive the simulator while performing a randomized sequence of activities that included texting, calling, and reading from a phone while they were driving, during which the accelerometer and gyroscope in the phone were logging sensory data. By extracting features from this sensory data, we then implemented a machine learning technique based on random forests to detect distracted driving. Our technique achieves very good precision, recall, and F -measure across all environmental conditions we tested. We believe that our contributions in this paper can have a significant impact on enhancing road safety.

Learning analytics is an emerging discipline focused on the measurement, collection, analysis and reporting of learner interaction data through the E-learning contents. Serious game provides a potential source for relevant educational user data; it can propose an interactive environment for training and offer an effective learning process. This paper presents methods and approaches of educational data mining such as EM and K-Means to discuss the learning analytics through serious games, and then we provide an analysis of the player experience data collected from the educational game “ELISA” used to teach students of biology the immunological technique for determination of ANTI-HIV antibodies. Finally, we propose critically evaluation of our results including the limitations of our study and making suggestions for future research that links learning analytics and serious gaming.