The massive adoption of hand-held devices has led to the explosion of mobile traffic volumes traversing home and enterprise networks, as well as the Internet. Traffic Classification (TC), i.e. the set of procedures for inferring (mobile) applications generating such traffic, has become nowadays the enabler for highly-valuable profiling information (with certain privacy downsides), other than being the workhorse for service differentiation/blocking. Nonetheless, the design of accurate classifiers is exacerbated by the raising adoption of encrypted protocols (such as TLS), hindering the suitability of (effective) deep packet inspection approaches. Also, the fast-expanding set of apps and the moving-target nature of mobile traffic makes design solutions with usual machine learning, based on manually-and expert-originated features, outdated and unable to keep the pace. For these reasons Deep Learning (DL) is here proposed, for the first time, as a viable strategy to design practical mobile traffic classifiers based on automatically-extracted features, able to cope with encrypted traffic, and reflecting their complex traffic patterns. To this end, different state-of-the-art DL techniques from (standard) TC are here reproduced, dissected (highlighting critical choices), and set into a systematic framework for comparison, including also a performance evaluation workbench. The latter outcome, although declined in the mobile context, has the applicability appeal to the wider umbrella of encrypted TC tasks. Finally, the performance of these DL classifiers is critically investigated based on an exhaustive experimental validation (based on three mobile datasets of real human users’ activity), highlighting the related pitfalls, design guidelines, and challenges.

A new industrial revolution is undergoing, based on a number of technological paradigms. The will to foster and guide this phenomenon has been summarized in the expression “Industry 4.0” (I4.0). Initiatives under this term share the vision that many key technologies underlying Cyber-Physical Systems and Big Data Analytics are converging to a new distributed, highly automated, and highly dynamic production network, and that this process needs regulatory and cultural advancements to effectively and timely develop. In this work, we focus on the technological aspect only, highlighting the unprecedented complexity of I4.0 emerging from the scientific literature. While previous works have focused on one or up to four related enablers, we consider ten technological enablers, including besides the most cited Big Data, Internet of Things, and Cloud Computing, also others more rarely considered as Fog and Mobile Computing, Artificial Intelligence, Human-Computer Interaction, Robotics, down to the often overlooked, very recent, or taken for granted Open-Source Software, Blockchain, and the Internet. For each we explore the main characteristics in relation to I4.0 and its interdependencies with other enablers. Finally we provide a detailed analysis of challenges in leveraging each of the enablers in I4.0, evidencing possible roadblocks to be overcome and pointing at possible future directions of research. Our goal is to provide a reference for the experts in some of the technological fields involved, for a reconnaissance of integration and hybridization possibilities with other fields in the endeavor of I4.0, as well as for the laymen, for a high-level grasp of the variety (and often deep history) of the scientific research backing I4.0.

The development of autonomous connected vehicles, moving as a platoon formation, is a hot topic in the intelligent transportation system (ITS) research field. It is on the road and deployment requires the design of distributed control strategies, leveraging secure vehicular ad-hoc networks (VANETs). Indeed, wireless communication networks can be affected by various security vulnerabilities and cyberattacks leading to dangerous implications for cooperative driving safety. Control design can play an important role in providing both resilience and robustness to vehicular networks. To this aim, in this article, we tackle and solve the problem of cyber-secure tracking for a platoon that moves as a cohesive formation along a single lane undergoing different kinds of cyber threats, that is, application layer and network layer attacks, as well as network induced phenomena. The proposed cooperative approach leverages an adaptive synchronization-based control algorithm that embeds a distributed mitigation mechanism of malicious information. The closed-loop stability is analytically demonstrated by using the Lyapunov-Krasovskii theory, while its effectiveness in coping with the most relevant type of cyber threats is disclosed by using PLEXE, a high fidelity simulator which provides a realistic simulation of cooperative driving systems.

Software-Defined Networking (SDN) and Blockchain are leading technologies used worldwide to establish safe network communication as well as build secure network infrastructures. They provide a robust and reliable platform to address threats and face challenges such as security, privacy, flexibility, scalability, and confidentiality. Driven by these assumptions, this paper presents an optimized energy-efficient and secure Blockchain-based software-defined IoT framework for smart networks. Indeed, SDN and Blockchain technologies have proven to be able to suitably manage resource utilization and to develop secure network communication across the IoT ecosystem. However, there is a lack of research works that present a comprehensive definition of such a framework that can meet the requirements of the IoT ecosystem (i.e. efficient energy utilization and reduced end-to-end delay). Therefore, in this research, we present a layered hierarchical architecture for the deployment of a distributed yet efficient Blockchain-enabled SDN-IoT framework that ensures efficient cluster-head selection and secure network communication via the identification and isolation of rouge switches. Besides, the Blockchain-enabled flow-rules record keeps track of the rules enforced in the switches and maintains the consistency within the controller cluster. Finally, we assess the performance of the proposed framework in a simulation environment and show that it can achieve optimized energy-utilization, end-to-end delay, and throughput compared to considered baselines, thus being able to achieve efficiency and security in the smart network.

The increasing diffusion of mobile devices has dramatically changed the network traffic landscape, with Traffic Classification (TC) surging into a fundamental role while facing new and unprecedented challenges. The recent and appealing adoption of Deep Learning (DL) techniques has risen as the solution overcoming the performance of ML techniques based on tedious and time-consuming handcrafted feature design. Still, the black-box nature of DL models prevents its practical and trustful adoption in critical scenarios where the reliability/interpretation of results/policies is of key importance. To cope with these limitations, eXplainable Artificial Intelligence (XAI) techniques have recently acquired the interest of the community. Accordingly, in this work we investigate trustworthiness and interpretability via XAI-based techniques to understand, interpret and improve the behavior of state-of-the-art multimodal DL traffic classifiers. The proposed methodology, as opposed to common results seen in XAI, attempts to provide global interpretation, rather than sample-based ones. Results, based on an open dataset, allow to complement the above findings with domain knowledge.

Cooperative driving is an essential component of future intelligent road systems. It promises greater safety, reducing accidents due to drivers distraction, improved infrastructure utilization, and fuel consumption reduction with platooning applications. Proper platoon management requires Inter-Vehicular Communication (IVC), longitudinal control and lateral control for stability and safety, and proper application protocols and algorithms to manage platoons and perform coordinated maneuvers. This work shows how a longitudinal controller based on distributed consensus can, at the same time, guarantee stability and performance in regime platoon operations, and be at the hearth of maneuvering protocols and algorithms, as it remains stable in face of changes of platoon topology and control gains. The adoption of a single control algorithm for two fundamental tasks greatly simplify the overall design of the system and improves stability and safety as it is not required to switch between different controllers during platoon operation. The theoretical properties are proven in the first part of the paper. The second part of the paper is devoted to its implementation in a state-ofthe- art mobility and IVC simulator, which is used for an extensive experimental campaign showing the dynamic properties of the system and its performance in a set of typical platoon maneuvers as join, leave and inclusion of a vehicle in the middle of the platoon. All simulations include realistic details of the vehicle dynamics (mass, dimensions, power train dynamics) as well as extremely detailed modeling of the communication network, from IEEE 802.11p protocol details, to collisions, packet errors, path loss and fading on the channel, and source-destination based delays.