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In today’s digital landscape, critical services are increasingly dependent on network connectivity, thus cybersecurity has become paramount. Indeed, the constant escalation of cyberattacks, including zero-day exploits, poses a significant threat. While Network Intrusion Detection Systems (NIDSs) leveraging machine-learning and deep-learning models have proven effective in recent studies, they encounter limitations such as the need for abundant samples of malicious traffic and full retraining upon encountering new attacks. These limitations hinder their adaptability in real-world scenarios. To address these challenges, we design a novel NIDS capable of promptly adapting to classify new attacks and provide timely predictions. Our proposal for attack-traffic classification adopts Few-Shot Class-Incremental Learning (FSCIL) and is based on the Rethinking Few-Shot (RFS) approach, which we experimentally prove to overcome other FSCIL state-of-the-art alternatives based on either meta-learning or transfer learning. We evaluate the proposed NIDS across a wide array of cyberattacks whose traffic is collected in recent publicly available datasets to demonstrate its robustness across diverse network-attack scenarios, including malicious activities in an Internet-of-Things context and cyberattacks targeting servers. We validate various design choices as well, involving the number of traffic samples per attack available, the impact of the features used to represent the traffic objects, and the time to deliver the classification verdict. Experimental results witness that our proposed NIDS effectively retains previously acquired knowledge (with over 94% F1-score) while adapting to new attacks with only few samples available (with over 98% F1-score). Thus, it outperforms non-FSCIL state of the art in terms of classification effectiveness and adaptation time. Moreover, our NIDS exhibits high performance even with traffic collected within short time frames, achieving 95% F1-score while reducing the time-to-insight. Finally, we identify possible limitations likely arising in specific application contexts and envision promising research avenues to mitigate them.

Significant in lifestyle have reshaped the Internet landscape, resulting in notable shifts in both the magnitude of Internet traffic and the diversity of apps utilized. The increased adoption of communication-and-collaboration apps, also fueled by lockdowns in the COVID pandemic years, has heavily impacted the management of network infrastructures and their traffic. A notable characteristic of these apps is their multi-activity nature, e.g., they can be used for chat and (interactive) audio/video in the same usage session: predicting and managing the traffic they generate is an important but especially challenging task. In this study, we focus on real data from four popular apps belonging to the aforementioned category: Skype, Teams, Webex, and Zoom. First, we collect traffic data from these apps, reliably label it with both the app and the specific user activity and analyze it from the perspective of traffic prediction. Second, we design data-driven models to predict this traffic at the finest granularity (i.e., at packet level) employing four advanced multitask deep learning architectures and investigating three different training strategies. The trade-off between performance and complexity is explored as well. We publish the dataset and release our code as open source to foster the replicability of our analysis. Third, we leverage the packet-level prediction approach to perform aggregate prediction at different timescales. Fourth, our study pioneers the trustworthiness analysis of these predictors via the application of eXplainable Artificial Intelligence to $(a)$ interpret their forecasting results and $(b)$ evaluate their reliability, highlighting the relative importance of different parts of observed traffic and thus offering insights for future analyses and applications. The insights gained from the analysis provided with this work have implications for various network management tasks, including monitoring, planning, resource allocation, and enforcing security policies.

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 internet of things is a worldwide technological development in communications. Low Power and Lossy Networks (LLN) are a fundamental part of the internet of things with numerous monitoring and controlling applications. This network has many challenges as well, due to limited hardware and communication resources, which causes problems in applications such as routing, connections, data transfer, and aggregation between nodes. The IETF group has provided a routing model for LLN networks, which expands IPv6 protocol based on Routing Protocol (RPL). The pro-posed decision system DDSLA-RPL creates a list of limited k member optimal parents based on qualitatively effective parameters such as hop, link quality, SNR rate, and ETX energy consumption, by informing child nodes of their connection link to available parents. In the routing section, a decision system approach based on learning automata has been proposed to dynamically determine and update the weight of influential parameters in routing. The effective parameters in the routing phase of DDSLA-RPL include battery depletion index, connection delay, and node queuing and throughput. The results of the simulation show that the proposed method outperforms other methods by about 30, 17, 20, 18, and 24 percent in mean longevity and energy efficiency, graph sustainability, operational power and latency, packet delivery rate test, and finally number of control messages test, respectively.

Cloud computing concepts offer effective and efficient tools for addressing resource-hungry computational problems. While conventional methods, architectures, and processing techniques may limit cloud data center performance, software-defined cloud computing (SDCC) is an approach where virtualization services to all network resources in a dc are software-defined and where software-defined networking (SDN) and cloud computing go hand in hand. SDCC-related concepts change the previous state of affairs by promoting the centralized control of networking functions in a data center. A key objective of developing software-driven cloud infrastructure is that the networking hardware, software, storage, security, and network traffic management is open and interoperable. This facilitates easy installation and management of networking functions in the cloud infrastructure. Employing SDCC concepts to cloud data centers can improve resource administration challenges to a greater extent. This paper presents a survey on SDCC. We begin by introducing SDCC environments and explain its main architectural components. We identify the essential contributions of various developments to this field and discuss the implementation challenges and limitations faced in their adoption. We also explore the potential of SDCC in two domains, namely, resource orchestration and application development, as case studies of specific interest. In an attempt to anticipate the future evolution, we discuss the important research opportunities and challenges in this promising field.

The population of cities is increasing day-by-day. According to United Nations, it is estimated that by 2050, 66% of the world’s population will live in cities [item 1) of the Appendix]. This is indicative of a drive to live in more privileged and smarter environments. Therefore, there exists an increased demand for intelligent and sustainable environments that offer citizens of urban areas a high quality life. Smart cities may be a solution. Millions of dedicated and reliable sensors are required in smart cities to enhance the quality of urban living. Communications infrastructure is inevitably required for connecting these sensors. In order to better manage urban resources, there exists a need to explore issues like deployment of sensors, communications technologies, information management, and defining and deploying proper smart city applications.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2008.09.024 Byline: Thomas Silverston (a), Olivier Fourmaux (a), Alessio Botta (b), Alberto Dainotti (b), Antonio Pescape (b), Giorgio Ventre (b), Kave Salamatian (c) Keywords: Peer-to-peer IPTV communities; Traffic analysis; Internet measurement; Experimental testbeds Abstract: The Internet is currently experiencing one of the most important challenges in terms of content distribution since its first uses as a medium for content delivery: users from passive downloaders and browsers are moving towards content producers and publishers. They often distribute and retrieve multimedia contents establishing network communities. This is the case of peer-to-peer IPTV communities. In this work we present a detailed study of P2P IPTV traffic, providing useful insights on both transport- and packet-level properties as well as on the behavior of the peers inside the network. In particular, we provide novel results on the (i) ports and protocols used; (ii) differences between signaling and video traffic; (iii) behavior of the traffic at different time scales; (iv) differences between TCP and UDP traffic; (v) traffic generated and received by peers; (vi) peers neighborhood and session duration. The knowledge gained thanks to this analysis is useful for several tasks, e.g. traffic identification, understanding the performance of different P2P IPTV technologies and the impact of such traffic on network nodes and links, and building more realistic models for simulations. 1 The work described in this paper has been funded by the CONTENT EU Network of Excellence (IST-FP6-038423). .sup.1 Author Affiliation: (a) UPMC Univ Paris 06, UMR 7606, LIP6/CNRS, F-75005, Paris, France (b) University of Napoli Federico II, Department of Computer Engineering and Systems, Via Claudio 21, I-80125 Napoli, NA, Italy (c) Lancaster University, InfoLab21, LA1 4WA, United Kingdom

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.comnet.2008.06.015 Byline: Alessio Botta, Antonio Pescape, Giorgio Ventre Keywords: Quality of service parameters; Network measurements; Heterogeneous wired-wireless networks Abstract: Today's networks are becoming increasingly complex and the ability to effectively and efficiently operate and manage them is ever more challenging. Ways to provide end-to-end Quality of Service have to cope with the increasing heterogeneity of these networks, which is due to the several actors of current network scenarios, from access networks to end-user devices, from protocols to applications and operating systems. Exploiting such heterogeneity, in this paper we present an approach to the identification of each element composing an end-to-end path. Such identification is useful in several situations. For instance, it can improve the performance of adaptive and network-aware applications, it can help intelligent routing approaches, and it can be used in network and service overlay scenarios. Our approach, based on Bayesian classifiers, utilizes the measurements and off-line processing of three QoS parameters, that are delay, jitter, and packet loss. To illustrate the capabilities of our proposal, we present the results of a large set of experimentations performed with both different sets of features and different sets of QoS parameters. Using measures related to various time periods, in which the considered paths presented diverse characteristics, we show that a blind identification of network bricks is possible and that its results present a good degree of generalizability. Author Affiliation: Dipartimento di Informatica e Sistemistica, University of Napoli "Federico II" Via Claudio, 21-80125 Napoli, Italy Article History: Received 14 December 2007; Revised 24 June 2008; Accepted 24 June 2008 Article Note: (footnote) [star] This work has been partially supported by CONTENT EU Network of Excellence (FP6-2006-IST-038423), by OneLab EU Project, and finally from the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 216585 (INTERSECTION Project)., [star][star] Preliminary results within the same framework of this work have been recently published in .

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. [ABSTRACT FROM AUTHOR]

Heterogeneous wireless networks that are used for seamless mobility are expected to face prominent problems in future fifth generation (5G) cellular networks. Due to their proper flexibility and adaptable preparation, remote-controlled unmanned aerial vehicles (UAVs) could assist heterogeneous wireless communication. However, the key challenges of current UAV-assisted communications consist in having appropriate accessibility over wireless networks via mobile devices with an acceptable Quality of Service grounded on the users’ preferences. To this end, we propose a novel method based on cooperative game theory to select the best UAV during handover process and optimize handover among UAVs by decreasing the i) end-to-end delay, ii) handover latency, and iii) signaling overheads. Moreover, the standard design of software-defined network with media-independent handover is used as forwarding switches in order to obtain seamless mobility. Numerical results derived from the real data are provided to illustrate the effectiveness of the proposed approach in terms of number of handovers, cost, and delay. [ABSTRACT FROM AUTHOR]

Modeling network traffic is an endeavor actively carried on since early digital communications, supporting a number of practical applications, that range from network planning and provisioning to security. Accordingly, many theoretical and empirical approaches have been proposed in this long-standing research, most notably, Machine Learning (ML) ones. Indeed, recent interest from network equipment vendors is sparking around the evaluation of solid information-theoretical modeling approaches complementary to ML ones, especially applied to new network traffic profiles stemming from the massive diffusion of mobile apps. To cater to these needs, we analyze mobile-app traffic available in the public dataset MIRAGE-2019 adopting two related modeling approaches based on the well-known methodological toolset of Markov models (namely, Markov Chains and Hidden Markov Models). We propose a novel heuristic to reconstruct application-layer messages in the common case of encrypted traffic. We discuss and experimentally evaluate the suitability of the provided modeling approaches for different tasks: characterization of network traffic (at different granularities, such as application, application category, and application version), and prediction of network traffic at both packet and message level. We also compare the results with several ML approaches, showing performance comparable to a state-of-the-art ML predictor (Random Forest Regressor). Also, with this work we provide a viable and theoretically sound traffic-analysis toolset to help improving ML evaluation (and possibly its design), and a sensible and interpretable baseline. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]