On the Integration of Blockchain and SDN: Overview, Applications, and Future Perspectives

Blockchain (BC) and software-defined networking (SDN) are leading technologies which have recently found applications in several network-related scenarios and have consequently experienced a growing interest in the research community. Indeed, current networks connect a massive number of objects over the Internet and in this complex scenario, to ensure security, privacy, confidentiality, and programmability, the utilization of BC and SDN have been successfully proposed. In this work, we provide a comprehensive survey regarding these two recent research trends and review the related state-of-the-art literature. We first describe the main features of each technology and discuss their most common and used variants. Furthermore, we envision the integration of such technologies to jointly take advantage of these latter efficiently. Indeed, we consider their group-wise utilization—named BC–SDN—based on the need for stronger security and privacy. Additionally, we cover the application fields of these technologies both individually and combined. Finally, we discuss the open issues of reviewed research and describe potential directions for future avenues regarding the integration of BC and SDN. To summarize, the contribution of the present survey spans from an overview of the literature background on BC and SDN to the discussion of the benefits and limitations of BC–SDN integration in different fields, which also raises open challenges and possible future avenues examined herein. To the best of our knowledge, compared to existing surveys, this is the first work that analyzes the aforementioned aspects in light of a broad BC–SDN integration, with a specific focus on security and privacy issues in actual utilization scenarios

Encrypted Multitask Traffic Classification via Multimodal Deep Learning

Traffic Classification (TC), i.e. the collection of procedures for inferring applications and/or services generating network traffic, represents the workhorse for service management and the enabler for valuable profiling information. Sadly, the growing trend toward encrypted protocols (e.g. TLS) and the evolving nature of network traffic make TC design solutions based on payload-inspection and machine learning, respectively, unsuitable. Conversely, Deep Learning (DL) is currently foreseen as a viable means to design traffic classifiers based on automatically-extracted features, reflecting the complex patterns distilled from the multifaceted (encrypted) traffic nature, implicitly carrying information in multimodal fashion. To this end, in this paper a novel multimodal DL approach for multitask TC is explored. The latter is able to capitalize traffic data heterogeneity (by learning both intra- and inter-modality dependencies), overcome performance limitations of existing (myopic) single-modality DL-based TC proposals, and solve different traffic categorization problems associated with different providers' desiderata. Based on a real dataset of encrypted traffic, we report performance gains of our proposal over (a) state-of-art multitask DL architectures and (b) multitask extensions of single-task DL baselines (both based on single-modality philosophy)

Unveiling MIMETIC: Interpreting deep learning traffic classifiers via XAI techniques

The widespread use of powerful mobile devices has deeply affected the mix of traffic traversing both the Internet and enterprise networks (with bring-your-own-device policies). Traffic encryption has become extremely common, and the quick proliferation of mobile apps and their simple distribution and update have created a specifically challenging scenario for traffic classification and its uses, especially network-security related ones. The recent rise of Deep Learning (DL) has responded to this challenge, by providing a solution to the time-consuming and human-limited handcrafted feature design, and better clas-sification performance. The counterpart of the advantages is the lack of interpretability of these black-box approaches, limiting or preventing their adoption in contexts where the reliability of results, or interpretability of polices is necessary. To cope with these limitations, eXplainable Artificial Intelligence (XAI) techniques have seen recent intensive research. Along these lines, our work applies XAI-based techniques (namely, Deep SHAP) to interpret the behavior of a state-of-the-art multimodal DL traffic classifier. As opposed to common results seen in XAI, we aim at a global interpretation, rather than sample-based ones. The results quantify the importance of each modality (payload- or header-based), and of specific subsets of inputs (e.g., TLS SNI and TCP Window Size) in determining the classification outcome, down to per-class (viz. application) level. The analysis is based on a publicly-released recent dataset focused on mobile app traffic

A Comparison of Machine and Deep Learning Models for Detection and Classification of Android Malware Traffic

With the increasing popularity of mobile-app services, malicious software is increasing as well. Accordingly, the interest of the scientific community in Machine and Deep Learning solutions for detecting and classifying malware traffic is growing. In this work, we provide a fair assessment of the performance of a number of data-driven strategies to detect and classify Android malware traffic. Three models are taken into account (Decision Tree, Random Forest, and 1-D Convolutional Neural Network) considering both flat (i.e. non-hierarchical) and hierarchical approaches. The experimental analysis performed using a state-of-art dataset (CIC-AAGM2017) reports that Random Forest exhibits the best performance in a flat setup, while moving to a hierarchical approach could cause significant variation in precision and recall. Such results push for further investigating advanced hierarchical setups and learning schemes

XAI Meets Mobile Traffic Classification: Understanding and Improving Multimodal Deep Learning Architectures

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

Machine and Deep Learning Approaches for IoT Attack Classification

In recent years, Internet of Things (IoT) traffic has increased dramatically and is expected to grow further in the next future. Because of their vulnerabilities, IoT devices are often the target of cyber-attacks with dramatic consequences. For this reason, there is a strong need for powerful tools to guarantee a good level of security in IoT networks. Machine and deep learning approaches promise good performance for such a complex task. In this work, we employ state-of-art traffic classifiers based on deep learning and assess their effectiveness in accomplishing IoT attack classification. We aim to recognize different attack classes and distinguish them from benign network traffic. In more detail, we utilize effective and unbiased input data that allow fast (i.e. 'early') detection of anomalies and we compare performance with that of traditional (i.e. 'postmortem') machine learning classifiers. The experimental results highlight the need for advanced deep learning architectures fed with input data specifically tailored and designed for IoT attack classification. Furthermore, we perform an occlusion analysis to assess the influence on the performance of some network layer fields and the possible bias they may introduce

Le scuole di una comunità emiliana nel Rinascimento tra religione e politica. Il caso di Novellara

The Schools of an Emilian Renaissance Community between Religion and Politics. The Case of Novellara This paper inquires the links and the mutual influence between the political and educational structures of the rural community of Novellara during the XVI century. The analysis will focus in particular on the evolution of these structures from the late medieval humanistic school system to the new post-Tridentine catholic school system. This dynamic will also be taken into account under the perspective of the political strategies undertaken by the ruling family of this community, a cadet branch of the Gonzaga family, in order to allow the survival of their little state. The only way to achieve this purpose was to put the fathers of the Society of Jesus in charge of the local schools