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.Comment: 42 pages, 14 figures, to be published in Journal of Network and Systems Management - Special Issue on Blockchains and Distributed Ledgers in Network and Service Managemen

How India Censors the Web

One of the primary ways in which India engages in online censorship is by ordering Internet Service Providers (ISPs) operating in its jurisdiction to block access to certain websites for its users. This paper reports the different techniques Indian ISPs are using to censor websites, and investigates whether website blocklists are consistent across ISPs. We propose a suite of tests that prove more robust than previous work in detecting DNS and HTTP based censorship. Our tests also discern the use of SNI inspection for blocking websites, which is previously undocumented in the Indian context. Using information from court orders, user reports, and public and leaked government orders, we compile the largest known list of potentially blocked websites in India. We pass this list to our tests and run them from connections of six different ISPs, which together serve more than 98% of Internet users in India. Our findings not only confirm that ISPs are using different techniques to block websites, but also demonstrate that different ISPs are not blocking the same websites

Methodologies for Mobile and Encrypted Traffic Classification via Machine Learning Approaches

The widespread use of handheld devices (e.g., smartphones) has led to a significant evolution in the way the users connect to the Internet and access contents or services. This entails a substantial change in the nature of network traffic. Traffic classification---the set of techniques suited to infer the applications generating network traffic---is currently the enabler for gathering valuable information for different stakeholders in the Internet traffic delivery supply chain. This includes its application for network management (e.g., service differentiation/blocking and quality-of-service enforcement), network security, and user profiling. On top of that, traffic classification highlights compelling privacy issues related to (the share of) this information in thorny scenarios (e.g., healthcare apps and enterprise environments). Nonetheless, the proliferation of encryption (e.g., anonymity tools) hinders the suitability of solutions based on cleartext traffic inspection and thus challenges current classifiers. Also, the moving-target nature of mobile traffic, due to the daily-expanding set of apps sharing common third-party services, accelerates the performance degradation of design solutions based on standard machine learning approaches. As such, this Thesis presents a set of novel methodologies for mobile traffic classification that can operate under the encrypted-traffic assumption and advances the state-of-the-art from multiple viewpoints. In detail, the present dissertation devises innovative machine learning approaches based on multi- and hierarchical-classification. Furthermore, it pioneers the adoption of the deep learning paradigm to design practical and effective mobile traffic classifiers through the automatic extraction of features reflecting complex data patterns. Then, to overcome the complexity of these solutions, a distributed deployment based on the big-data framework is investigated. Such analysis highlights the non-transparent nature of the big-data accelerator when applied to the training phase of deep learning classifiers, shedding light on intrinsic trade-offs. Extensive experimental evaluations are conducted to assess the performance of proposed approaches and compare them with most related state-of-the-art solutions. This goal is achieved by the definition of a common benchmark encompassing public datasets. In this regard, a novel architecture is designed and implemented to capture and label our publicly-released dataset

Toward Effective Mobile Encrypted Traffic Classification through Deep Learning

Traffic Classification (TC), consisting in how to infer applications generating network traffic, is currently the enabler for valuable profiling information, other than being the workhorse for service differentiation/blocking. Further, TC is fostered by the blooming of mobile (mostly encrypted) traffic volumes, fueled by the huge adoption of hand-held devices. While researchers and network operators still rely on machine learning to pursue accurate inference, we envision Deep Learning (DL) paradigm as the stepping stone toward the design of practical (and effective) mobile traffic classifiers based on automatically-extracted features, able to operate with encrypted traffic, and reflecting complex traffic patterns. In this context, the paper contribution is fourfold. First, it provides a taxonomy of the key network traffic analysis subjects where DL is foreseen as attractive. Secondly, it delves into the non-trivial adoption of DL to mobile TC, surfacing potential gains. Thirdly, to capitalize such gains, it proposes and validates a general framework for DL-based encrypted TC. Two concrete instances originating from our framework are then experimentally evaluated on three mobile datasets of human users’ activity. Lastly, our framework is leveraged to point to future research perspectives

Human behavior sensing: challenges and approaches

In recent years, Activities of Daily Living Scale (ADLs) is widely used to evaluate living abilities of the patients and the elderly. So, the study of behavior sensing has attracted more and more attention of researchers. Behavior sensing technology is of strong theoretical and practical value in the fields of smart home and virtual reality. Most of the currently proposed approaches for tracking indicators of ADLs are human-centric, which classify activities using physical information of the observed persons. Considering the privacy concerns of the human-centric approaches (e.g. images of home environment, private behavior), researchers have also proposed some thing-centric approaches, which use environmental information on things (e.g. the vibration of things) to infer human activity. In this paper, by considering the unified steps in both the human-centric approaches and the thing-centric approaches, we make a comprehensive survey on the challenges and proposed methods to do behavior sensing, which are signal collection, preprocessing, feature extraction, and activity recognition. Moreover, based on the latest research progress, we post a perspective from our standpoint, discussing future outlook and challenges of human behavior sensing