Vehicular communication (VC) systems are being developed primarily to enhance transportation safety and efficiency. Vehicle-to-vehicle communication, in particular, frequent cooperative awareness messages or safety beacons, has been considered over the past years as a main approach. Meanwhile, the need to provide security and to safeguard users' privacy is well understood, and security architectures for VC systems have been proposed. Although technical approaches to secure VC have several commonalities and a consensus has formed, there are critical questions that have remained largely unanswered: Are the proposed security and privacy schemes practical? Can the secured VC systems support the VC-enabled applications as effectively as unsecured VC would? How should security be designed so that its integration into a VC system has a limited effect on the system's performance? In this paper, we provide answers to these questions, investigating the joint effect of a set of system parameters and components. We consider the state-of-the-art approach in secure VC, and we evaluate analytically and through simulations the interdependencies among components and system characteristics. Overall, we identify key design choices for the deployment of efficient, effective, and secure VC systems.

Nowadays, malware has become an epidemic problem. Among the attacks exploiting the computer resources of victims, one that has become usual is related to the massive amounts of computational resources needed for digital currency cryptomining. Cybercriminals steal computer resources from victims, associating these resources to the crypto-currency mining pools they benefit from. This research work focuses on offering a solution for detecting such abusive cryptomining activity, just by means of passive network monitoring. To this end, we identify a new set of highly relevant network flow features to be used jointly with a rich set of machine and deep-learning models for real-time cryptomining flow detection. We deployed a complex and realistic cryptomining scenario for training and testing machine and deep learning models, in which clients interact with real servers across the Internet and use encrypted connections. A complete set of experiments were carried out to demonstrate that, using a combination of these highly informative features with complex machine learning models, cryptomining attacks can be detected on the wire with telco-grade precision and accuracy, even if the traffic is encrypted.

This paper introduces an approach toward the automatic enforcement of security policies in network functions virtualization (NFV) networks and dynamic adaptation to network changes. The approach relies on a refinement model that allows the dynamic transformation of high-level security requirements into configuration settings for the network security functions (NSFs), and optimization models that allow the optimal selection of the NSFs to use. These models are built on a formalization of the NSF capabilities, which serves to unequivocally describe what NSFs are able to do for security policy enforcement purposes. The approach proposed is the first step toward a security policy aware NFV management, orchestration, and resource allocation system--a paradigm shift for the management of virtualized networks--and it requires minor changes to the current NFV architecture. We prove that our approach is feasible, as it has been implemented by extending the OpenMANO framework and validated on several network scenarios. Furthermore, we prove with performance tests that policy refinement scales well enough to support current and future virtualized networks.