Scalability and security restrictions are posing new challenges for blockchain networks, especially in the face of Distributed Denial-of-Service (DDoS) attacks and upcoming quantum threats. Previous research also found that post-quantum blockchains, despite their improved cryptographic algorithms, are still vulnerable to DDoS attacks, emphasizing the need for more resilient architectural solutions. This research studies the use of dynamic sharding, an innovative approach for post-quantum blockchains that allows for adaptive division of the network into shards based on workload and network conditions. Unlike static sharding, dynamic sharding optimizes resource allocation in real time, increasing transaction throughput and minimizing DDoS-induced disruptions. We provide a detailed study using Monte Carlo simulations to examine transaction success rates, resource consumption, and fault tolerance for both dynamic sharding-based and non-sharded post-quantum blockchains under simulated DDoS attack scenarios. The findings show that dynamic sharding leads to higher transaction success rates and more efficient resource use than non-sharded infrastructures, even in high-intensity attack scenarios. Furthermore, the combination of dynamic sharding and the Falcon post-quantum signature technique creates a layered strategy that combines cryptographic robustness, scalability, and resilience. This paper provides light on the potential of adaptive blockchain designs to address major scalability and security issues, opening the path for quantum-resilient systems.

Learners are more active and interactive interactive in social media as they freely express themselves and share their thoughts and feeling which generates much useful information about their characteristics. However, the same learners are not usually interactive in MOOCs as much as they do in social media. Therefore, focusing on the Learner’s social profile is a key component towards a successful and enriched experience of learning. Especially that MOOCs offer the possibility to sign up using social media accounts. Thereby, profile modelling is an essential step to capture the knowledge about the learners. In this paper, we aim to explore the information generated by the learner’s participation in social media and combine it with his characteristics in MOOCs. Our approach is based on implementing an ontological learner profile that combines other ontologies (IMS-LIP standard, FOAF, and SIOC ontology) as well as some of the major concepts that describe a user in social media. The result of our solution is a social profile ontology (SPOnt) that describes the main concepts of a learner in each environment (MOOCs and social media). We rely on ontology mapping and merging techniques to maintain the semantic links between the different concepts of the two implemented ontologies.

We witness, today, a strong evolution of learning environments. In parallel, a problem has emerged, consisting in how to capitalize the production of resources when switching from one environment to another. The heterogeneity of the environments, the evolution of the platforms and the will to reuse the educational resources already produced pushed us to design an intelligent system based on cases. In this study, we will focus on the need for resource indexing to facilitate the task of researching and recommending educational resources for authors regardless of the learning environment used. In the literature, this representation can take two forms: Standards or ontologies. The use of standards has partially solved our problem since it is very beneficial for systems that are under construction. On the other hand, it is more interesting to go through the ontologies for systems that are already designed, that we wish to reuse, especially for those that have shown, through the authors, a great satisfaction in the field of knowledge management. Indeed, their use does not require an investment in the environments concerned by the reuse.

Network traffic analysis (NTA) is a technique used by network administrators to monitor network activity, ensure availability, and detect unusual patterns to identify potential anomalies. However, traditional traffic monitoring systems often struggle to detect these anomalies accurately because they rely on rigid models and a limited pool of data. Additionally, anomaly detection is particularly challenging, as anomalies exhibit patterns that differ from most network activities, making their identification based on prior knowledge difficult. This underscores the necessity for an automated and unsupervised approach capable of detecting various types of anomalies despite these limitations. In this paper, we propose an unsupervised framework that combines three statistical machine learning (ML) methods for mobile network failure detection, such as the lower control limit, the cumulative sum algorithm (CUSUM), and the robust stat detector model. Compared to previous studies, models often struggle with large datasets and generate high rates of false positives. However, our approach has proven to be better suited to the demands of traffic monitoring in large telecommunications infrastructures. It offers improved data handling and significantly reduces the rate of false positives while achieving an impressive 98% detection rate for anomalies on telecommunications sites.