Résumé: In today’s digital age, the rapid increase in online activities has heightened web users’ privacy risks, making them more susceptible to various cyber-attacks. Among these, phishing is one of the most widespread threats in cybersecurity. Phishing employs deceitful tactics to lure individuals and organizations into visiting malicious URLs and disclosing sensitive information, such as passwords, credit card details, and personal data. These attacks are carefully crafted to mislead users into believing they are interacting with legitimate websites or online services, to steal account information for malicious purposes. To address the limitations of traditional phishing detection methods—such as their inability to detect zero-day attacks, high rates of false positives and negatives, and the need for frequent updates to list-based approaches (e.g., blacklists and whitelists)—this paper presents an advanced approach for identifying malicious URLs by integrating transformer learning and deep learning techniques. Specifically, Distilled Bidirectional Encoder Representations from Transformers (DistilBERT) extracts features from URLs and captures relevant textual information. A hybrid deep learning model combining convolutional neural network (CNN) and long short-term memory (LSTM) layers is then applied to classify the dataset into malicious and legitimate URLs. The novelty of this method lies in combining DistilBERT with a hybrid deep learning model (CNN-LSTM) to detect malicious URLs. The proposed approach was evaluated on two large public datasets to test its effectiveness under various conditions. Experimental results demonstrate that it performs exceptionally well, achieving the highest accuracy of 98.19%, and outperforming some comparable methods in the literature on similar datasets. This proves that the integration of DistilBERT and the hybrid CNN-LSTM model is successful in detecting malicious URLs. DistilBERT optimizes the feature extraction process, while the CNN-LSTM model leverages the strengths of both models and mitigates the limitations of each, resulting in a more comprehensive approach to classification.

Résumé: In contemporary times, the proliferation of phishing attacks presents a substantial and growing challenge to cybersecurity. This fraudulent tactic is designed to deceive unsuspecting individuals, enticing them to access malicious websites and disclose sensitive personal information such as usernames, passwords, and financial details. As a result, malevolent actors exploit this data for illicit purposes. As the sophistication and maliciousness of phishing continue to evolve, researchers are earnestly developing multiple anti-phishing solutions in the literature. Among these solutions, those based on machine learning and deep learning models have gained substantial attention in recent years. This study proposes an intelligent mechanism to detect phishing URLs. The proposed system is based on the permutation importance method to select the most relevant URL features and the SMOTE-Tomek link method to solve …

Résumé: Traditional education systems do not provide students with much freedom to choose the right training of study that suits them, which leads on long-term to the negative effects not only on social, economic and mental’ well-being of student, but also will have a negative effect on the quality of the work produced by this student in the future. In addition, skills and talents in a specific area refer to general skills and working life practices. This paper presents a pedagogical orientation system based on blockchain technology and machine learning that accurately predicts the future specialized training and profession in which the common core student will have more opportunity to succeed. Python programming language has been used to implement the suggested pedagogical orienting mechanism in two key steps. (1) In the profiles matching step we have used the incidence matrix to correlate the professional and training profiles. (2) The student ranking issue was resolved at the ranking profiles step using the learning to rank methodology. The data used to feed the various machine learning algorithms in our system are stored on the blockchain, representing the academic results of 320 students at the end of the common core training in the computer science department of 20th august 1955 university in Skikda, Algeria, which served as a database for subsequent experiments and served to confirm the system's feasibility and credibility. The proposed system has demonstrated its effectiveness by accurately predicting the future specialized training compatible with the student's scientific capabilities, allowing him to succeed and excel. Also, it has contributed to reducing the possibility of falsifying results and certificates while enhancing security and transparency through blockchain protocols. Finally, this system encourages all participants to trust the automatic orientation process, which makes it possible to improve the issues experienced in the fields of education by enhancing interactions between the student, the university, and the employing industries during the educational orientation process.

Résumé: While some studies indicate that flipped classrooms offer many positive educational outcomes, other studies draw attention to limitations associated with flipped classroom (students' limited preparation prior to class, students' need for guidance at home, students' inability to get immediate feedback while they study at home, and little research has focused on students' learning outcomes, such as: satisfaction and motivation). This paper attempts to address several of these limitations through exploratory studies conducted in an Algerian University. The approach proposed in this paper called Flipped classroom based on Context-Aware mobile learning system (FC-CAMLS) aims to provide learners with an adapted course content format based on their feedback and context. The latter has a significant influence on multimedia content in adaptive mobile learning. The system was implemented in an English Language course. It was expected that the FC-CAMLS increased the management of students' heterogeneity. A quantitative analysis by means of structural equation modeling was performed to analyze the causal relationships between knowledge, skills, motivation and students' satisfaction. The results show that the system has positive effects on students' knowledge, skills, and motivation. Finally, our research provides useful results that the use of the context dimensions and learner's feedback in adaptive mobile learning is more beneficial for learners especially in the flipped classroom.

Résumé: The approach proposed in this paper called Flipped classroom based on Context-Aware mobile learning system (FC-CAMLS ) aims to provide learners with an adapted course content format based on their feedback and context. The latter has a significant influence on multimedia content in adaptive mobile learning. The contribution was applied in the context of the flipped learning in order to manage the heterogeneity of context imposed by this approach. Firstly, we present a quantitative analysis by means of structural equation modeling to analyze the causal relationships of knowledge, skills, and motivation with students' satisfaction. Secondly, we confirm that the proposed flipped classroom has positive effects on students' knowledge, skills, and motivation. Finally, our research provides useful results that the use of the context dimensions and learner’s feedback in adaptive mobile learning is more beneficial for learners especially in the flipped classroom.

Résumé: Blockchain is a shared database that builds trust without third-party involvement. Its decentralized architecture ensures data is dispersed and cannot be wiped. This makes blockchain highly suitable for e-learning and online education, which have become primary means for obtaining certifications and degrees, especially with the surge in online learning due to the COVID-19 pandemic. The increase in online universities and platforms has led to issues with fraudulent coursework, poor evaluations, and degree verification. This project proposes a blockchain-based strategy for the education sector, using smart contracts to manage student data and provide success certifications.

Résumé: One of the toughest cybersecurity issues today are insider threats for which no good solution has been provided to limit their damage through commonly used security solutions. Which could cause serious damage to the assets of the organization. This article begins by presenting a comprehensive multidisciplinary survey of insider threats and the various AI technologies in use. Then, we proposed a method to detect internal threats by analyzing user behavior. A series of activities and events that are analyzed to extract features to effectively detect malicious activity from normal activity. The selected feature vectors are used to train the model. The convolutional neural network (CNN) is used during the implementation phase to detect insider threats of fixed-size feature vectors. The data used to conduct the experiments is public data related to internal threats cert r4.2. The experimental results of the proposed model show that it can successfully detect insider threats with an accuracy of 0.9558 in the best cases.