Real-life room-escape games are ludic activities in which participants enter a room in order to get out of it only after solving some riddles. In this paper, we explain a Room Escape teaching experience developed in the Engineering School at Universitat Autònoma de Barcelona. The goal of this activity is to increase student’s motivation and to improve their learning on two courses of the second year in the Computer Engineering degree: Computer Networksand Information and Security.

In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications’ perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers’ knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks.

In order to improve the forwarding performance of the Information-Centric Networking (ICN) paradigm in wireless ad hoc environments, different nodes’ properties, such as geographical position and topological centrality, have been proposed as metrics to identify the suitability of a node as a forwarder. However, while these metrics may be efficient in terms of node selection, they can breach the privacy of the node itself. The more efficient these metrics are, the more private information they may reveal. In this letter, we propose a privacy-preserving ICN forwarding scheme based on homomorphic encryption for wireless ad hoc networks.

This article proposes an enhanced PRIvacy preserVing Opportunistic routing protocol (ePRIVO) for Vehicular Delay-Tolerant Networks (VDTN). ePRIVO models a VDTN as a time-varying neighboring graph where edges correspond to neighboring relationship between pairs of vehicles. It addresses the problem of vehicles taking routing decision meanwhile keeping their information private, i.e, vehicles compute their similarity and/or compare their routing metrics in a private manner using the Paillier homomorphic encryption scheme. The effectiveness of ePRIVO is supported through extensive simulations with synthetic mobility models and a real mobility trace. Simulation results show that ePRIVO presents on average very low cryptographic costs in most scenarios. Additionally, ePRIVO presents on average gains of approximately 29% and 238% in terms of delivery ratio for the real and synthetic scenarios considered compared to other privacy-preserving routing protocols.