The Internet of Things (IoT) is rapidly growing, with an estimated 14.4 billion active endpoints in 2022 and a forecast of approximately 30 billion connected devices by 2027. This proliferation of IoT devices has come with significant security challenges, including intrinsic security vulnerabilities, limited computing power, and the absence of timely security updates. Attacks leveraging such shortcomings could lead to severe consequences, including data breaches and potential disruptions to critical infrastructures. In response to these challenges, this research paper presents the IoT Proxy, a modular component designed to create a more resilient and secure IoT environment, especially in resource-limited scenarios. The core idea behind the IoT Proxy is to externalize security-related aspects of IoT devices by channeling their traffic through a secure network gateway equipped with different Virtual Network Security Functions (VNSFs). Our solution includes a Virtual Private Network (VPN) terminator and an Intrusion Prevention System (IPS) that uses a machine learning-based technique called oblivious authentication to identify connected devices. The IoT Proxy’s modular, scalable, and externalized security approach creates a more resilient and secure IoT environment, especially for resource-limited IoT devices. The promising experimental results from laboratory testing demonstrate the suitability of IoT Proxy to secure real-world IoT ecosystems.

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.

The coming advent of Quantum Computing promises to jeopardize current communications security, undermining the effectiveness of traditional public-key based cryptography. Different strategies (Post-Quantum or Quantum Cryptography) have been proposed to address this problem. Many techniques and algorithms based on quantum phenomena have been presented in recent years; the most relevant example is the introduction of Quantum Key Distribution (QKD). This approach allows to exchange cryptographic keys among parties and does not suffer from the development of quantum computation. Problems arise when this technique has to be deployed and combined with modern distributed infrastructures that heavily depend on cloud and virtualisation paradigms. This paper addresses the issue by presenting a new software stack that effortlessly introduces QKD in such environments. This software stack allows for agnostic integration, monitoring, and management of QKD, independent from a specific vendor or technology. Furthermore, a QKD simulator is presented, designed, and tested. This latter contribution is suitable as a low-level testing device, as an independent software module to check QKD protocols, and as a testbed to identify future practical enhancements.

This paper presents a classification of the anomalies that can appear when designing or implementing communication protection policies. Together with the already known intra- and inter-policy anomaly types, we introduce a novel category, the inter-technology anomalies, related to security controls implementing different technologies, both within the same network node and among different network nodes. Through an empirical assessment, we prove the practical significance of detecting this new anomaly class. Furthermore, this paper introduces a formal model, based on first-order logic rules that analyses the network topology and the security controls at each node to identify the detected anomalies and suggest the strategies to resolve them. This formal model has manageable computational complexity and its implementation has shown excellent performance and good scalability.