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Since its launch, Velib' (the Bike Sharing System -BSS-in Paris) has emerged in the Parisian landscape and has been a model for similar systems in many cities. A major problem with BSS is the stations' heterogeneity caused by the attractivity of some stations located in particular areas. In this paper, we focus on spatial outliers defined as stations having a behavior significantly different from their neighboring stations. First, we propose an improved version of Moran scatterplot to exploit the similarity between neighbors, and we test it on a real dataset issued from Velib' system to identify outliers. Then, we design a new method that globally improves the resources' availability in bike stations by adapting the users' trips to the resources' availability. Results show that with a partial collaboration of the users or a limitation to the rush hours, the proposed method enhances significantly the resources' availability in Velib' system.

Over the past years, blockchain technology has become more and more interesting since its ability to carry out transactions without any mediator. To ensure the transactions' reliability, consensus algorithms are adopted by blockchain technology. However, due to the failure of consensus algorithms in managing nodes' identities, blockchain technology is considered inappropriate for many applications. In this article, we propose the permissionless proof‐of‐reputation‐X (PL‐PoRX) that upgrades an existing consensus algorithm, the proof‐of‐reputation‐X (PoRX). PL‐PoRX replaces the trusted identity database in PoRX with a new admission process to make the algorithm suitable for permissionless blockchains, while maintaining PoRX's reputation mechanism. Several experiments are conducted to show the efficiency of our approach under different scenarios. We also debate the security model of the new protocol and study its time complexity. The results show that PL‐PoRX decreases the number of blocks issued by malicious miners, and help benign miners build reputation faster.

With the Internet's unprecedented growth and nations' reliance on computer networks, new cyber‐attacks are created every day as means for achieving financial gain, imposing political agendas, and developing cyberwarfare arsenals. Network security is thus acquiring increasing attention among researchers, practitioners, network architects, policy makers, and others. To defend organizations' networks from existing, foreseen, and future threats, intrusion detection systems (IDSs) are becoming a must. Existing surveys on anomaly‐based IDS (AIDS) focus on specific components such as detection mechanisms and lack many others. In contrast to existing surveys, this article covers the full scope needed by researchers and practitioners alike when studying AIDS. The scope ranges from the intrusion detection techniques to attacks forms and passing through the relevant attack features, most‐used datasets, challenges, and potential solutions. This article provides an exhaustive review of IDSs and discusses their requirements and performance metrics in deep. It presents a taxonomy of IDSs based on four criteria: information source, detection strategy, detection mode, and architecture. Then, in‐depth analysis and a comparison of network intrusion detection approaches based on anomaly detection techniques are given. The article also introduces a classification of computer network attacks, along with their different forms and the relevant network traffic features to detect them, as well as a summary of the popular datasets used by the researchers to evaluate the IDSs. Finally, the article highlights several research challenges and the possible solutions to deal with them.

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