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Future autonomous transportation systems necessitate network infrastructure capable of accommodating massive vehicular connectivity, despite the scarce availability of frequency resources. Current approaches for achieving such required high spectral efficiency, rely on the utilization of Multiple-Input, Multiple-Output (MIMO) technology. However, conventional MIMO processing approaches, based on linear processing principles, leave much of the system's capacity heavily unexploited. They typically require a large number of power-consuming antennas and RF-chains to support a substantially smaller number of concurrently connected devices, even when the devices are transmitting at low rates. This translates to inflated operational costs that become substantial, particularly in ultra-dense, metropolitan-scale deployments. Therefore, the question is how to efficiently harness this unexploited MIMO capacity and fully leverage the available RF infrastructure to maximize device connectivity. Addressing this challenge, this work proposes an Open Radio Access Network (Open-RAN) deployment, with Massively Parallelizable Non-linear (MPNL) MIMO processing for densely deployed, and power-efficient vehicular networks. For the first time, we quantify the substantial gains of MPNL in achieving massive vehicular connectivity with significantly reduced utilized antennas, compared to conventional linear approaches, and without any throughput loss. We find that an Open-RAN-based realization exploiting the MPNL advancements can yield an increase of over 300% in terms of concurrently transmitting single-antenna vehicles in urban mobility settings and for various Vehicle-to-Infrastructure (V2I) and Network (V2N) use cases. In this context, we discuss how implementing MPNL allows for simpler and more densely deployed radio units, paving the way for fully autonomous and sustainable transportation systems., Comment: Presented in IEEE PIMRC '24

Digital twins (DTs) have emerged as a promising enabler for representing the real-time states of physical worlds and realizing self-sustaining systems. In practice, DTs of physical devices, such as mobile users (MUs), are commonly deployed in multi-access edge computing (MEC) networks for the sake of reducing latency. To ensure the accuracy and fidelity of DTs, it is essential for MUs to regularly synchronize their status with their DTs. However, MU mobility introduces significant challenges to DT synchronization. Firstly, MU mobility triggers DT migration which could cause synchronization failures. Secondly, MUs require frequent synchronization with their DTs to ensure DT fidelity. Nonetheless, DT migration among MEC servers, caused by MU mobility, may occur infrequently. Accordingly, we propose a two-timescale DT synchronization and migration framework with reliability consideration by establishing a non-convex stochastic problem to minimize the long-term average energy consumption of MUs. We use Lyapunov theory to convert the reliability constraints and reformulate the new problem as a partially observable Markov decision-making process (POMDP). Furthermore, we develop a heterogeneous agent proximal policy optimization with Beta distribution (Beta-HAPPO) method to solve it. Numerical results show that our proposed Beta-HAPPO method achieves significant improvements in energy savings when compared with other benchmarks., Comment: 15 pages, 14 figures

Nowadays, the new network architectures with respect to the traditional network topologies must manage more data, which entails having increasingly robust and scalable network structures. As there is a process of growth, adaptability, and change in traditional data networks, faced with the management of large volumes of information, it is necessary to incorporate the virtualization of network functions in the context of information content networks, in such a way that there is a balance between the user and the provider at cost level and profit, the functions on the network. In turn, NFVs (Network Functions Virtualization) are considered network structures designed based on IT virtualization technologies, which allow virtualizing of the functions that can be found in the network nodes, which are connected through routing tables, which allows offering communication services for various types of customers. Therefore, information-centric networks (IC, unlike traditional data networks which proceed to exchange information between hosts using data packets and TCP/IP communication protocols, use the content of the data for this purpose where the data travels through the network is stored in a routing table located in the CR (Content Router) of the router temporality to be reused later, which allows for reducing operation costs and capital costs . The purpose of this work is to analyze how the virtualization of network functions is integrated into the field of information-centric networks. Also, the advantages and disadvantages of both architectures are considered and presented as a critical analysis when considering the current difficulties and future trends of both network topologies., Comment: 7 pages,7 figures, conference paper, index in Scopus

This work proposes a provably convergent and low complexity optimization algorithm for the maximization of the secrecy energy efficiency in the uplink of a wireless network aided by a Reconfigurable Intelligent Surface (RIS), in the presence of an eavesdropper. The mobil users' transmit powers and the RIS reflection coefficients are optimized. Numerical results show the performance of the proposed methods and compare the use of active and nearly-passive RISs from an energy-efficient perspective., Comment: Comments: 6 pages, 4 figures, presented at IEEE ICC 2024, Denver, CO, USA

The rapid expansion of cloud services and their unpredictable workload demands present significant challenges in resource management. Traditional resource management approaches, primarily based on static rules and thresholds, often fail to ensure cost-effectiveness and optimal resource utilization. This research introduces a predictive model designed to forecast traffic demand, aiming to shift from a reactive to a proactive resource management approach. By integrating advanced predictive analytics with the capabilities of P4 programmable switches, this study seeks to enhance the efficiency of resource utilization and improve system robustness. The goal is to equip organizations with the agility and economic efficiency required to navigate the complexities of dynamic cloud environments effectively. This approach not only promises to refine microservice resource allocation but also supports the broader objective of fostering more resilient and efficient cloud infrastructures., Comment: 15 pages

The emerging technology of quantum computing has the potential to change the way how problems will be solved in the future. This work presents a centralized network control algorithm executable on already existing quantum computer which are based on the principle of quantum annealing like the D-Wave Advantage. We introduce a resource reoccupation algorithm for traffic engineering in wide-area networks. The proposed optimization algorithm changes traffic steering and resource allocation in case of overloaded transceivers. Settings of active components like fiber amplifiers and transceivers are not changed for the reason of stability. This algorithm is beneficial in situations when the network traffic is fluctuating in time scales of seconds or spontaneous bursts occur. Further, we developed a discrete-time flow simulator to study the algorithm's performance in wide-area networks. Our network simulator considers backlog and loss modeling of buffered transmission lines. Concurring flows are handled equally in case of a backlog. This work provides an ILP-based network configuring algorithm that is applicable on quantum annealing computers. We showcase, that traffic losses can be reduced significantly by a factor of 2 if a resource reoccupation algorithm is applied in a network with bursty traffic. As resources are used more efficiently by reoccupation in heavy load situations, overprovisioning of networks can be reduced. Thus, this new form of network operation leads toward a zero-margin network. We show that our newly introduced network simulator enables analyses of short-time effects like buffering within fat-pipe networks. As the calculation of network configurations in real-sized networks is typically time-consuming, quantum computing can enable the proposed network configuration algorithm for application in real-sized wide-area networks., Comment: 7 pages, 9 figures, accepted for publication at "IEEE International Conference on High Performance Switching and Routing 2024"

The Open Radio Access Network (O-RAN) architecture empowers intelligent and automated optimization of the RAN through applications deployed on the RAN Intelligent Controller (RIC) platform, enabling capabilities beyond what is achievable with traditional RAN solutions. Within this paradigm, Traffic Steering (TS) emerges as a pivotal RIC application that focuses on optimizing cell-level mobility settings in near-real-time, aiming to significantly improve network spectral efficiency. In this paper, we design a novel TS algorithm based on a Cascade Reinforcement Learning (CaRL) framework. We propose state space factorization and policy decomposition to reduce the need for large models and well-labeled datasets. For each sub-state space, an RL sub-policy will be trained to learn an optimized mapping onto the action space. To apply CaRL on new network regions, we propose a knowledge transfer approach to initialize a new sub-policy based on knowledge learned by the trained policies. To evaluate CaRL, we build a data-driven and scalable RIC digital twin (DT) that is modeled using important real-world data, including network configuration, user geo-distribution, and traffic demand, among others, from a tier-1 mobile operator in the US. We evaluate CaRL on two DT scenarios representing two network clusters in two different cities and compare its performance with the business-as-usual (BAU) policy and other competing optimization approaches using heuristic and Q-table algorithms. Benchmarking results show that CaRL performs the best and improves the average cluster-aggregated downlink throughput over the BAU policy by 24% and 18% in these two scenarios, respectively., Comment: 14 pages, 10 figures

Internet of Things devices can now be found everywhere, including in our households in the form of Smart Home networks. Despite their ubiquity, their security is unsatisfactory, as demonstrated by recent attacks. The IETF's MUD standard has as goal to simplify and automate the secure deployment of end devices in networks. A MUD file contains a device specific description of allowed network activities (e.g., allowed IP ports or host addresses) and can be used to configure for example a firewall. A major weakness of MUD is that it is not expressive enough to describe traffic patterns representing device interactions, which often occur in modern Smart Home platforms. In this article, we present a new language for describing such traffic patterns. The language allows writing device profiles that are more expressive than MUD files and take into account the interdependencies of traffic connections. We show how these profiles can be translated to efficient code for a lightweight firewall leveraging NFTables to block non-conforming traffic. We evaluate our approach on traffic generated by various Smart Home devices, and show that our system can accurately block unwanted traffic while inducing negligible latency., Comment: 13 pages of body text, 16 pages total

Software-Defined Networking (SDN) significantly simplifies programming, reconfiguring, and optimizing network devices, such as switches and routers. The de facto standard for programmming SDN devices is the P4 language. However, the flexibility and power of P4, and SDN more generally, gives rise to important risks. As a number of incidents at major cloud providers have shown, errors in SDN programs can compromise the availability of networks, leaving them in a non-functional state. The focus of this paper are errors in control-plane programs that interact with P4-enabled network devices via the standardized P4Runtime API. For clients of the P4Runtime API it is easy to make mistakes that lead to catastrophic failures, despite the use of Google's Protocol Buffers as an interface definition language. This paper proposes P4R-Type, a novel verified P4Runtime API for Scala that performs static checks for P4 control plane operations, ruling out mismatches between P4 tables, allowed actions, and action parameters. As a formal foundation of P4R-Type, we present the $F_{\text{P4R}}$ calculus and its typing system, which ensure that well-typed programs never get stuck by issuing invalid P4Runtime operations. We evaluate the safety and flexibility of P4R-Type with 3 case studies. To the best of our knowledge, this is the first work that formalises P4Runtime control plane applications, and a typing discipline ensuring the correctness of P4Runtime operations., Comment: 82 pages, 27 figures, extended version of paper to be published at OOPSLA 2023

Network Slicing (NS) is an essential technique extensively used in 5G networks computing strategies, mobile edge computing, mobile cloud computing, and verticals like the Internet of Vehicles and industrial IoT, among others. NS is foreseen as one of the leading enablers for 6G futuristic and highly demanding applications since it allows the optimization and customization of scarce and disputed resources among dynamic, demanding clients with highly distinct application requirements. Various standardization organizations, like 3GPP's proposal for new generation networks and state-of-the-art 5G/6G research projects, are proposing new NS architectures. However, new NS architectures have to deal with an extensive range of requirements that inherently result in having NS architecture proposals typically fulfilling the needs of specific sets of domains with commonalities. The Slicing Future Internet Infrastructures (SFI2) architecture proposal explores the gap resulting from the diversity of NS architectures target domains by proposing a new NS reference architecture with a defined focus on integrating experimental networks and enhancing the NS architecture with Machine Learning (ML) native optimizations, energy-efficient slicing, and slicing-tailored security functionalities. The SFI2 architectural main contribution includes the utilization of the slice-as-a-service paradigm for end-to-end orchestration of resources across multi-domains and multi-technology experimental networks. In addition, the SFI2 reference architecture instantiations will enhance the multi-domain and multi-technology integrated experimental network deployment with native ML optimization, energy-efficient aware slicing, and slicing-tailored security functionalities for the practical domain., Comment: 10 pages, 11 figures

The separate receiver architecture with a time- or power-splitting mode, widely used for simultaneous wireless information and power transfer (SWIPT), has a major drawback: Energy-intensive local oscillators and mixers need to be installed in the information decoding (ID) component to downconvert radio frequency (RF) signals to baseband signals, resulting in high energy consumption. As a solution to this challenge, an integrated receiver (IR) architecture has been proposed, and, in turn, various SWIPT modulation schemes compatible with the IR architecture have been developed. However, to the best of our knowledge, no research has been conducted on modulation scheduling in SWIPT-based IoT sensor networks while taking into account the IR architecture. Accordingly, in this paper, we address this research gap by studying the problem of joint scheduling for unicast/multicast, IoT sensor, and modulation (UMSM) in a time-slotted SWIPT-based IoT sensor network system. To this end, we leverage mathematical modeling and optimization techniques, such as the Lagrangian duality and stochastic optimization theory, to develop an UMSM scheduling algorithm that maximizes the weighted sum of average unicast service throughput and harvested energy of IoT sensors, while ensuring the minimum average throughput of both multicast and unicast, as well as the minimum average harvested energy of IoT sensors. Finally, we demonstrate through extensive simulations that our UMSM scheduling algorithm achieves superior energy harvesting (EH) and throughput performance while ensuring the satisfaction of specified constraints well., Comment: 29 pages, 13 figures (eps)

This paper addresses the challenges of detecting anomalies in cellular networks in an interpretable way and proposes a new approach using variational autoencoders (VAEs) that learn interpretable representations of the latent space for each Key Performance Indicator (KPI) in the dataset. This enables the detection of anomalies based on reconstruction loss and Z-scores. We ensure the interpretability of the anomalies via additional information centroids (c) using the K-means algorithm to enhance representation learning. We evaluate the performance of the model by analyzing patterns in the latent dimension for specific KPIs and thereby demonstrate the interpretability and anomalies. The proposed framework offers a faster and autonomous solution for detecting anomalies in cellular networks and showcases the potential of deep learning-based algorithms in handling big data.

Deterministic real-time communication with bounded delay is an essential requirement for many safety-critical cyber-physical systems, and has received much attention from major standardization bodies such as IEEE and IETF. In particular, Ethernet technology has been extended by time-triggered scheduling mechanisms in standards like TTEthernet and Time-Sensitive Networking. Although the scheduling mechanisms have become part of standards, the traffic planning algorithms to create time-triggered schedules are still an open and challenging research question due to the problem's high complexity. In particular, so-called plug-and-produce scenarios require the ability to extend schedules on the fly within seconds. The need for scalable scheduling and routing algorithms is further supported by large-scale distributed real-time systems like smart energy grids with tight communication requirements. In this paper, we tackle this challenge by proposing two novel algorithms called Hierarchical Heuristic Scheduling (H2S) and Cost-Efficient Lazy Forwarding Scheduling (CELF) to calculate time-triggered schedules for TTEthernet. H2S and CELF are highly efficient and scalable, calculating schedules for more than 45,000 streams on random networks with 1,000 bridges as well as a realistic energy grid network within sub-seconds to seconds.

With the advent of 5G and the anticipated arrival of 6G, there has been a growing research interest in combining mobile networks with Non-Terrestrial Network platforms such as low earth orbit satellites and Geosynchronous Equatorial Orbit satellites to provide broader coverage for a wide range of applications. However, integrating these platforms is challenging because Line-Of-Sight (LOS) estimation is required for both inter satellite and satellite-to-terrestrial segment links. Machine Learning (ML) techniques have shown promise in channel modeling and LOS estimation, but they require large datasets for model training, which can be difficult to obtain. In addition, network operators may be reluctant to disclose their network data due to privacy concerns. Therefore, alternative data collection techniques are needed. In this paper, a framework is proposed that uses generative models to generate synthetic data for LOS estimation in non-terrestrial 6G networks. Specifically, the authors show that generative models can be trained with a small available dataset to generate large datasets that can be used to train ML models for LOS estimation. Furthermore, since the generated synthetic data does not contain identifying information of the original dataset, it can be made publicly available without violating privacy

In recent years, the open source operating system OpenWrt has become a popular option for replacing proprietary firmware on networking devices such as home routers or access points. In order to configure an OpenWrt system, like setting up firewall rules, the user has to either sign in to the web interface or use SSH to manually change configuration files on the device. While the current approach is sufficient for small home networks, it only allows for limited automation of management tasks and configuration management becomes time-consuming, for example, on larger campus networks where access control lists on OpenWrt access points need updates regularly. This paper describes our efforts to implement the RESTCONF configuration management protocol standardized by the IETF on OpenWrt systems that have limited CPU and memory resources. We detail our design choices that make our implementation resource efficient for the use cases we target and we compare our implementation against other similar solutions. Our implementation is available on GitHub under an open source license.

Hospital infrastructures are always in evidence in periods of crisis, such as natural disasters or pandemic events, under stress. The recent COVID-19 pandemic exposed several inefficiencies in hospital systems over a relatively long period. Among these inefficiencies are human factors, such as how to manage staff during periods of high demand, and technical factors, including the management of Portable Medical Devices (PMD), such as mechanical ventilators, capnography monitors, infusion pumps, or pulse oximeters. These devices, which are vital for monitoring patients or performing different procedures, were found to have a high turnover during high-demand, resulting in inefficiencies and more pressure on medical teams. Thus, the work PMD-Track evaluates in detail two popular indoor tracking approaches concerning their accuracy, placement of beacons, and economic impacts. The key novelty of PMD-Track relies on using smartphones provided to hospital employees, replacing typical stationary gateways spread across a hospital, functioning as mobile gateways with a front-end that assists staff in locating PMDs. As employees approach tagged PMDs, their smartphone automatically updates the location of spotted PMDs in real-time, providing room-level localization data with up to 83% accuracy for fingerprinting and 35% for multilateration. In addition, fingerprinting is 45% cheaper than multilateration over the course of five years. Practical experiments were evaluated based on two locations in Z\"urich, Switzerland., Comment: 18 pages

Recent advances in web technologies make it more difficult than ever to detect and block web tracking systems. In this work, we propose ASTrack, a novel approach to web tracking detection and removal. ASTrack uses an abstraction of the code structure based on Abstract Syntax Trees to selectively identify web tracking functionality shared across multiple web services. This new methodology allows us to: (i) effectively detect web tracking code even when using evasion techniques (e.g., obfuscation, minification, or webpackaging); and (ii) safely remove those portions of code related to tracking purposes without affecting the legitimate functionality of the website. Our evaluation with the top 10k most popular Internet domains shows that ASTrack can detect web tracking with high precision (98%), while discovering about 50k tracking code pieces and more than 3,400 new tracking URLs not previously recognized by most popular privacy-preserving tools (e.g., uBlock Origin). Moreover, ASTrack achieved a 36% reduction in functionality loss in comparison with the filter lists, one of the safest options available. Using a novel methodology that combines computer vision and manual inspection, we estimate that full functionality is preserved in more than 97% of the websites., Comment: This paper has been accepted for publication at IEEE INFOCOM 2023. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses

This paper describes the active available bandwidth measurement tool DietTopp. It measures the available bandwidth and the link capacity on an end-to-end path having one bottleneck link. DietTopp is based on a simplified TOPP method. This paper describe and motivate the simplifications and assumptions made to TOPP. Further, the paper describes some of the DietTopp implementation issues. A first evaluation of DietTopp in a testbed scenario is made. Within this evaluation the performance and measurement accuracy of DietTopp is compared to the state-of-the-art tools Pathload and Pathrate. We show that DietTopp gives fast and accurate estimations of both the available bandwidth and the link capacity of the bottleneck link., Comment: 5 pages, Second Swedish National Computer Networking Workshop (SNCNW), Vol. 5, 2004

This article shows a brief history of Techno-Economic Assessment (TEA) in Communications, a proposed redefinition of TEA as well as the new challenges derived from a dynamic context with cloud-native virtualized networks, the Helium Network & alike blockchain-based decentralized networks, the new network as a platform (NaaP) paradigm, carbon pricing, network sharing, and web3, metaverse and blockchain technologies. The authors formulate the research question and show the need to improve TEA models to integrate and manage all this increasing complexity. This paper also proposes the characteristics TEA models should have and their current degree of compliance for several use cases: 5G and beyond, software-defined wide area network (SD-WAN), secure access service edge (SASE), secure service edge (SSE), and cloud cybersecurity risk assessment. The authors also present TEA extensibility to request for proposals (RFP) processes and other industries, to conclude that there is an urgent need for agile and effective TEA in Comms that allows industrialization of agile decision-making for all market stakeholders to choose the optimal solution for any technology, scenario and use case., Comment: 18 pages, 1 figure, 2 tables

Efficient data transfers over high-speed, long-distance shared networks require proper utilization of available network bandwidth. Using parallel TCP streams enables an application to utilize network parallelism and can improve transfer throughput; however, finding the optimum number of parallel TCP streams is challenging due to nondeterministic background traffic sharing the same network. Additionally, the non-stationary, multi-objectiveness, and partially-observable nature of network signals in the host systems add extra complexity in finding the current network condition. In this work, we present a novel approach to finding the optimum number of parallel TCP streams using deep reinforcement learning (RL). We devise a learning-based algorithm capable of generalizing different network conditions and utilizing the available network bandwidth intelligently. Contrary to rule-based heuristics that do not generalize well in unknown network scenarios, our RL-based solution can dynamically discover and adapt the parallel TCP stream numbers to maximize the network bandwidth utilization without congesting the network and ensure fairness among contending transfers. We extensively evaluated our RL-based algorithm's performance, comparing it with several state-of-the-art online optimization algorithms. The results show that our RL-based algorithm can find near-optimal solutions 40% faster while achieving up to 15% higher throughput. We also show that, unlike a greedy algorithm, our devised RL-based algorithm can avoid network congestion and fairly share the available network resources among contending transfers., Comment: Submitted to ICC 2023, converted to 12 pages , conference submission was for 7 pages

Anomaly detection research works generally propose algorithms or end-to-end systems that are designed to automatically discover outliers in a dataset or a stream. While literature abounds concerning algorithms or the definition of metrics for better evaluation, the quality of the ground truth against which they are evaluated is seldom questioned. In this paper, we present a systematic analysis of available public (and additionally our private) ground truth for anomaly detection in the context of network environments, where data is intrinsically temporal, multivariate and, in particular, exhibits spatial properties, which, to the best of our knowledge, we are the first to explore. Our analysis reveals that, while anomalies are, by definition, temporally rare events, their spatial characterization clearly shows some type of anomalies are significantly more popular than others. We find that simple clustering can reduce the need for human labeling by a factor of 2x-10x, that we are first to quantitatively analyze in the wild., Comment: Published in the International Teletraffic Congress (ITC 34), 14-16 September 2022

In this paper, we present the modular design and implementation of DONUT, a novel tool for identifying software running on a device. Our tool uses a rule-based approach to detect software-specific DNS fingerprints (stored in an easily extendable database) in passively monitored DNS traffic. We automated the rule extraction process for DONUT with the help of ATLAS, a novel tool we developed for labeling network traffic by the software that created it. We demonstrate the functionality of our pipeline by generating rules for a number of applications, evaluate the performance as well as scalability of the analysis, and confirm the functional correctness of DONUT using an artificial data set for which the ground-truth is known. In addition, we evaluate DONUT's analysis results on a large real-world data set with unknown ground truth., Comment: 10 pages, 9 figures

Today, the audit and diagnosis of the causal relationships between the events in a trigger-action-based event chain (e.g., why is a light turned on in a smart home?) in the Internet of Things (IoT) platforms are untrustworthy and unreliable. The current IoT platforms lack techniques for transparent and tamper-proof ordering of events due to their device-centric logging mechanism. In this paper, we develop a framework that facilitates tamper-proof transparency and event order in an IoT platform by proposing a Blockchain protocol and adopting the vector clock system, both tailored for the resource-constrained heterogeneous IoT devices, respectively. To cope with the unsuited storage (e.g., ledger) and computing power (e.g., proof of work puzzle) requirements of the Blockchain in the commercial off-the-shelf IoT devices, we propose a partial consistent cut protocol and engineer a modular arithmetic-based lightweight proof of work puzzle, respectively. To the best of our knowledge, this is the first Blockchain designed for resource-constrained heterogeneous IoT platforms. Our event ordering protocol based on the vector clock system is also novel for the IoT platforms. We implement our framework using an IoT gateway and 30 IoT devices. We experiment with 10 concurrent trigger-action-based event chains while each chain involves 20 devices, and each device participates in 5 different chains. The results show that our framework may order these events in 2.5 seconds while consuming only 140 mJ of energy per device. The results hence demonstrate the proposed platform as a practical choice for many IoT applications such as smart home, traffic monitoring, and crime investigation., Comment: 12 pages, 13 eps figures

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 Management

Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevents intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require an explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at a low cost creates fertile ground for the implementation of these techniques.

Network honeypots are often used by information security teams to measure the threat landscape in order to secure their networks. With the advancement of honeypot development, today's medium-interaction honeypots provide a way for security teams and researchers to deploy these active defense tools that require little maintenance on a variety of protocols. In this work, we deploy such honeypots on five different protocols on the public Internet and study the intent and sophistication of the attacks we observe. We then use the information gained to develop a clustering approach that identifies correlations in attacker behavior to discover IPs that are highly likely to be controlled by a single operator, illustrating the advantage of using these honeypots for data collection., Comment: In Proceedings of the 7th IEEE EuroS&P Workshop on Traffic Measurements for Cybersecurity (WTMC 2022)

The optimization of Value of Information (VoI) in sensor networks integrates awareness of the measured process in the communication system. However, most existing scheduling algorithms do not consider the specific needs of monitoring applications, but define VoI as a generic Mean Square Error (MSE) of the whole system state regardless of the relevance of individual components. In this work, we consider different summary statistics, i.e., different functions of the state, which can represent the useful information for a monitoring process, particularly in safety and industrial applications. We propose policies that minimize the estimation error for different summary statistics, showing significant gains by simulation., Comment: 5 pages - submitted to IEEE Networking Letters

State-of-the-art topologies for datacenters (DC) and high-performance computing (HPC) networks are demand-oblivious and static. Therefore, such network topologies are optimized for the worst-case traffic scenarios and can't take advantage of changing demand patterns when such exist. However, recent optical switching technologies enable the concept of dynamically reconfiguring circuit-switched topologies in real-time. This capability opens the door for the design of self-adjusting networks: networks with demand-aware and dynamic topologies in which links between nodes can be established and re-adjusted online and respond to evolving traffic patterns. This paper studies a recently proposed model for optical leaf-spine reconfigurable networks. We present a novel algorithm, GreedyEgoTrees, that dynamically changes the network topology. The algorithm greedily builds ego trees for nodes in the network, where nodes cooperate to help each other, taking into account the global needs of the network. We show that GreedyEgoTrees has nice theoretical properties, outperforms other possible algorithms (like static expander and greedy dynamic matching) and can significantly improve the average path length for real DC and HPC traces., Comment: 11 pages

QUIC offers security and privacy for modern web traffic by closely integrating encryption into its transport functionality. In this process, it hides transport layer information often used for network monitoring, thus obsoleting traditional measurement concepts. To still enable passive RTT estimations, QUIC introduces a dedicated measurement bit - the spin bit. While simple in its design, tracking the spin bit at line-rate can become challenging for software-based solutions. Dedicated hardware trackers are also unsuitable as the spin bit is not invariant and can change in the future. Thus, this paper investigates whether P4-programmable hardware, such as the Intel Tofino, can effectively track the spin bit at line-rate. We find that the core functionality of the spin bit can be realized easily, and our prototype has an accuracy close to software-based trackers. Our prototype further protects against faulty measurements caused by reordering and prepares the data according to the needs of network operators, e.g., by classifying samples into pre-defined RTT classes. Still, distinct concepts in QUIC, such as its connection ID, are challenging with current hardware capabilities.

We consider an information update system consisting of $N$ sources sending status packets at random instances according to a Poisson process to a remote monitor through a single server. We assume a heteregeneous server with exponentially distributed service times which is equipped with a waiting room holding the freshest packet from each source referred to as Single Buffer Per-Source Queueing (SBPSQ). The sources are assumed to be equally important, i.e., non-weighted average AoI is used as the information freshness metric, and subsequently two symmetric scheduling policies are studied in this paper, namely First Source First Serve (FSFS) and the Earliest Served First Serve (ESFS) policies, the latter policy being proposed the first time in the current paper to the best of our knowledge. By employing the theory of Markov Fluid Queues (MFQ), an analytical model is proposed to obtain the exact distribution of the Age of Information (AoI) for each source when the FSFS and ESFS policies are employed at the server. Subsequently, a benchmark scheduling-free scheme named as Single Buffer with Replacement (SBR) that uses a single one-packet buffer shared by all sources is also studied with a similar but less complex analytical model. We comparatively study the performance of the three schemes through numerical examples and show that the proposed ESFS policy outperforms the other two schemes in terms of the average AoI and the age violation probability averaged across all sources, in a scenario of sources possessing different traffic intensities but sharing a common service time., Comment: 16 pages, 7 figures

Internet of Things (IoT) defines a network of devices connected to the internet and sharing a massive amount of data between each other and a central location. These IoT devices are connected to a network therefore prone to attacks. Various management tasks and network operations such as security, intrusion detection, Quality-of-Service provisioning, performance monitoring, resource provisioning, and traffic engineering require traffic classification. Due to the ineffectiveness of traditional classification schemes, such as port-based and payload-based methods, researchers proposed machine learning-based traffic classification systems based on shallow neural networks. Furthermore, machine learning-based models incline to misclassify internet traffic due to improper feature selection. In this research, an efficient multilayer deep learning based classification system is presented to overcome these challenges that can classify internet traffic. To examine the performance of the proposed technique, Moore-dataset is used for training the classifier. The proposed scheme takes the pre-processed data and extracts the flow features using a deep neural network (DNN). In particular, the maximum entropy classifier is used to classify the internet traffic. The experimental results show that the proposed hybrid deep learning algorithm is effective and achieved high accuracy for internet traffic classification, i.e., 99.23%. Furthermore, the proposed algorithm achieved the highest accuracy compared to the support vector machine (SVM) based classification technique and k-nearest neighbours (KNNs) based classification technique., Comment: 14 pages, 4 figures, 11 tables, Accepted for publication in CMC-Computers, Materials & Continua

This paper presents HURRA, a system that aims to reduce the time spent by human operators in the process of network troubleshooting. To do so, it comprises two modules that are plugged after any anomaly detection algorithm: (i) a first attention mechanism, that ranks the present features in terms of their relation with the anomaly and (ii) a second module able to incorporates previous expert knowledge seamlessly, without any need of human interaction nor decisions. We show the efficacy of these simple processes on a collection of real router datasets obtained from tens of ISPs which exhibit a rich variety of anomalies and very heterogeneous set of KPIs, on which we gather manually annotated ground truth by the operator solving the troubleshooting ticket. Our experimental evaluation shows that (i) the proposed system is effective in achieving high levels of agreement with the expert, that (ii) even a simple statistical approach is able to extracting useful information from expert knowledge gained in past cases to further improve performance and finally that (iii) the main difficulty in live deployment concerns the automated selection of the anomaly detection algorithm and the tuning of its hyper-parameters.

The present paper shows a proposal of the characteristics Cloud Risk Assessment Models should have and presents the review of the literature considering those characteristics in order to identify current gaps. This work shows a ranking of Cloud RA models and their degree of compliance with the theoretical reference Cloud Risk Assessment model. The review of literature shows that RA approaches leveraging CSA (Cloud Security Alliance) STAR Registry that have into account organizations security requirements present higher degree of compliance, but they still lack risk economic quantification. The myriad of conceptual models, methodologies and frameworks although based on current NIST SP 800:30, ISO 27001, ISO 27005, ISO 30001, ENISA standards could be enhanced by the use of techno-economic models like UTEM, created by the author, in order to conceive more simplified models for effective Risk Assessment and Mitigation closer to the theoretical reference model for Cloud Risk Assessment, available for all cloud models (IaaS, PaaS, SaaS) and easy to use for all stakeholders., Comment: 12 pages, 5 figures, 2 tables

Network neutrality (NN) is a principle of equal treatment of data in network infrastructures with fairness and universality being the primary outcomes of the NN management practice. For networks, the accomplishment of NN management practice is essential to deal with heterogeneous user requirements and the ever-increasing data traffic. Current tools and methods address the NN problem by detecting network neutrality violations and detecting traffic differentiation. This paper proposes the NN-PCM (Network Neutrality Policy Conformance Module) that deploys the BEREC network neutrality policy using a bandwidth allocation model (BAM). The NN-PCM new approach allocates bandwidth to network users and accomplishes the BEREC NN policy concomitantly. Network neutrality is achieved by grouping users with similar traffic requirements in classes and leveraging the bandwidth allocation model's characteristics. The conceptual analysis and simulation results indicate that NN-PCM allocates bandwidth to users and accomplishes BEREC network neutrality conformance by design with transparent, non-discriminatory, exceptional, and proportional management practices., Comment: 17 pages, 8 figures, IJNM preprint

Low-energy Bluetooth devices have become ubiquitous and widely used for different applications. Among these, Bluetooth trackers are becoming popular as they allow users to track the location of their physical objects. To do so, Bluetooth trackers are often built-in within other commercial products connected to a larger crowdsourced tracking system. Such a system, however, can pose a threat to the security and privacy of the users, for instance, by revealing the location of a user's valuable object. In this paper, we introduce a set of security properties and investigate the state of commercial crowdsourced tracking systems, which present common design flaws that make them insecure. Leveraging the results of our investigation, we propose a new design for a secure crowdsourced tracking system (SECrow), which allows devices to leverage the benefits of the crowdsourced model without sacrificing security and privacy. Our preliminary evaluation shows that SECrow is a practical, secure, and effective crowdsourced tracking solution, Comment: 10 pages - ACM WiSec 2021 - Preprint

Many networked applications, e.g., in the domain of cyber-physical systems, require strict service guarantees, usually in the form of jitter and latency bounds, for time-triggered traffic flows. It is a notoriously hard problem to compute a network-wide traffic plan that satisfies these requirements, and dynamic changes in the flow set add even more challenges. Existing traffic-planning methods are ill-suited for dynamic scenarios because they either suffer from high computational cost, can result in low network utilization, or provide no explicit guarantees when transitioning to a new traffic plan that incorporates new flows. Therefore, we present a novel approach for dynamic traffic planning of time-triggered flows. Our conflict-graph based modeling of the traffic planning problem allows to reconfigure active flows to increase the network utilization, while also providing per-flow QoS guarantees during the transition to the new traffic plan. Additionally, we introduce a novel heuristic for computing the new traffic plans. Evaluations of our prototypical implementation show that we can efficiently compute new traffic plans in scenarios with hundreds of active flows for a wide range of scenarios.

This paper proposes the characteristics a techno-economic model for 5G should have considering both mobile network operators perspective and end users needs. It also presents a review and classification of models in the literature based on the characteristics of such theoretical techno-economic reference model. The performed analysis identifies current gaps in the techno-economic modeling literature for 5G architectures and shows it can be enhanced using agile techno-economic models like the Universal Techno-Economic Model (UTEM) created and developed by the author to industrialize assessment of different technological solutions, considering all market players perspectives and applicable to decision-making in multiple domains. This model can be used for an effective and agile 5G techno-economic assessment, including not only network deployment perspective but also customers and end users requirements as well as other stakeholders to select the most adequate 5G architectural solution considering both technical and economic feasibility. UTEM model is currently available for all industry stakeholders under specific license of use., Comment: 12 pages, 4 figures, 1 table

Substantial efforts are invested in improving network security, but the threat landscape is rapidly evolving, particularly with the recent interest in programmable network hardware. We explore a new security threat, from an attacker who has gained control of such devices. While it should be obvious that such attackers can trivially cause substantial damage, the challenge and novelty are in doing so while preventing quick diagnosis by the operator. We find that compromised programmable devices can easily degrade networked applications by orders of magnitude, while evading diagnosis by even the most sophisticated network diagnosis methods in deployment. Two key observations yield this result: (a) targeting a small number of packets is often enough to cause disproportionate performance degradation; and (b) new programmable hardware is an effective enabler of careful, selective targeting of packets. Our results also point to recommendations for minimizing the damage from such attacks, ranging from known, easy to implement techniques like encryption and redundant requests, to more complex considerations that would potentially limit some intended uses of programmable hardware. For data center contexts, we also discuss application-aware monitoring and response as a potential mitigation., Comment: 14 pages, 13 figures, 1 table

Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks, supporting network services with various QoS requirements, such as massive IoT and URLLC. In ORAN, network functionality is dis-aggregated into remote units (RUs), distributed units (DUs) and central units (CUs), which allows flexible software on Commercial-Off-The-Shelf (COTS) deployments. Furthermore, the mapping of variable RU requirements to local mobile edge computing centres for future centralized processing would significantly reduce the power consumption in cellular networks. In this paper, we study the RU-DU resource assignment problem in an ORAN system, modelled as a 2D bin packing problem. A deep reinforcement learning-based self-play approach is proposed to achieve efficient RU-DU resource management, with AlphaGo Zero inspired neural Monte-Carlo Tree Search (MCTS). Experiments on representative 2D bin packing environment and real sites data show that the self-play learning strategy achieves intelligent RU-DU resource assignment for different network conditions.

We present MatchKAT, an algebraic language for modeling match-action packet processing in network switches. Although the match-action paradigm has remained a popular low-level programming model for specifying packet forwarding behavior, little has been done towards giving it formal semantics. With MatchKAT, we hope to embark on the first steps in exploring how network programs compiled to match-action rules can be reasoned about formally in a reliable, algebraic way. In this paper, we give details of MatchKAT and its metatheory, as well as a formal treatment of match expressions on binary strings that form the basis of "match" in match-action. Through a correspondence with NetKAT, we show that MatchKAT's equational theory is sound and complete with regards to a similar packet filtering semantics. We also demonstrate the complexity of deciding equivalence in MatchKAT is PSPACE-complete., Comment: 15 pages, 2 figures

In this paper, the problem of vertical handover in software-defined network (SDN) based heterogeneous networks (HetNets) is studied. In the studied model, HetNets are required to offer diverse services for mobile users. Using an SDN controller, HetNets have the capability of managing users' access and mobility issues but still have the problems of ping-pong effect and service interruption during vertical handover. To solve these problems, a mobility-aware seamless handover method based on multipath transmission control protocol (MPTCP) is proposed. The proposed handover method is executed in the controller of the software-defined HetNets (SDHetNets) and consists of three steps: location prediction, network selection, and handover execution. In particular, the method first predicts the user's location in the next moment with an echo state network (ESN). Given the predicted location, the SDHetNet controller can determine the candidate network set for the handover to pre-allocate network wireless resources. Second, the target network is selected through fuzzy analytic hierarchical process (FAHP) algorithm, jointly considering user preferences, service requirements, network attributes, and user mobility patterns. Then, seamless handover is realized through the proposed MPTCP-based handover mechanism. Simulations using real-world user trajectory data from Korea Advanced Institute of Science & Technology show that the proposed method can reduce the handover times by 10.85% to 29.12% compared with traditional methods. The proposed method also maintains at least one MPTCP subflow connected during the handover process and achieves a seamless handover., Comment: 12 pages, 12 figures

In mobile edge computing (MEC), one of the important challenges is how much resources of which mobile edge server (MES) should be allocated to which user equipment (UE). The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only. This paper presents a novel comprehensive utility function for resource allocation in MEC. The utility function considers the heterogeneous nature of applications that a UE offloads to MES. The proposed utility function considers all important parameters, including CPU, RAM, hard disk space, required time, and distance, to calculate a more realistic utility value for MESs. Moreover, we improve upon some general algorithms, used for resource allocation in MEC and cloud computing, by considering our proposed utility function. We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes. The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources. The utility function depends upon the UE requests and the distance between UEs and MES, and serves as a realistic means of comparison between different types of UE requests. Choosing (or selecting) an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task. We show that MES resource allocation is sub-optimal if CPU is the only resource considered. By taking into account the other resources, i.e., RAM, disk space, request time, and distance in the utility function, we demonstrate improvement in the resource allocation algorithms in terms of service rate, utility, and MES energy consumption., Comment: 17 pages, 3 Figures, Published in Computers, Materials & Continua

Secret sharing was proposed primarily in 1979 to solve the problem of key distribution. In recent decades, researchers have proposed many improvement schemes. Among all these schemes, the verifiable multi-secret sharing (VMSS) schemes are studied sufficiently, which share multiple secrets simultaneously and perceive malicious dealer as well as participants. By pointing out that the schemes presented by Dehkordi and Mashhadi in 2008 cannot detect some vicious behaviors of the dealer, we propose two new VMSS schemes by adding validity check in the verification phase to overcome this drawback. Our new schemes are based on XTR public key system, and can realize $GF(p^{6})$ security by computations in $GF(p^{2})$ without explicit constructions of $GF(p^{6})$, where $p$ is a prime. Compared with the VMSS schemes using RSA and linear feedback shift register (LFSR) public key cryptosystems, our schemes can achieve the same security level with shorter parameters by using trace function. What's more, our schemes are much simpler to operate than those schemes based on Elliptic Curve Cryptography (ECC). In addition, our schemes are dynamic and threshold changeable, which means that it is efficient to implement our schemes according to the actual situation when participants, secrets or the threshold needs to be changed., Comment: 11 pages, 1 figure, 4 tables

With the increase of IoT devices and technologies coming into service, Malware has risen as a challenging threat with increased infection rates and levels of sophistication. Without strong security mechanisms, a huge amount of sensitive data is exposed to vulnerabilities, and therefore, easily abused by cybercriminals to perform several illegal activities. Thus, advanced network security mechanisms that are able of performing a real-time traffic analysis and mitigation of malicious traffic are required. To address this challenge, we are proposing a novel IoT malware traffic analysis approach using deep learning and visual representation for faster detection and classification of new malware (zero-day malware). The detection of malicious network traffic in the proposed approach works at the package level, significantly reducing the time of detection with promising results due to the deep learning technologies used. To evaluate our proposed method performance, a dataset is constructed which consists of 1000 pcap files of normal and malware traffic that are collected from different network traffic sources. The experimental results of Residual Neural Network (ResNet50) are very promising, providing a 94.50% accuracy rate for detection of malware traffic., Comment: 10 pages, 5 figures, 2 tables

In this paper, we introduce Internet Photonic Sensing (IPS), a new framework for deformation and vibration measurement and monitoring based on signals that are available from standard fiber optic communication hardware deployed in the Internet. IPS is based on the hypothesis that atmospheric, seismic, anthropogenic and other natural activity cause vibrations in the earth that trigger detectable changes in standard optical signals that transmit data through Internet fiber. We assume a simple system component model for optical communication hardware and identify two candidate signals that may reflect deformation and vibrations and that can be measured through standard interfaces: Optical Signal Strength (OSS) and Bit Error Rate (BER). We investigate the efficacy of IPS through a series of controlled, laboratory experiments that consider how the candidate signals respond when fiber is subjected to a range of stresses. We believe that IPS offers the potential to transform the practice of scientific, commercial and public safety-related vibration monitoring applications by providing a highly-sensitive platform that is available at a global scale., Comment: 7 pages, 9 figures

Cross-stakeholder service orchestration is a generalization of 5G network slices which has potential to increase business agility in Beyond 5G (B5G). An architectural framework is proposed which enables domain operators to expose their functionalities towards E2E services as capabilities. Capability orchestration is proposed as a mechanism for exposure. The use of intent-based management for communicating domain owner's business goals to capability orchestration is analyzed. The combination of business goal input and capability orchestration provides a basis for agile monetization of domain resources for domain owners, and a building block for rich end-to-end B5G services.

Recent advances in artificial intelligence (AI) offer an opportunity for the adoption of self-driving networks. However, network operators or home-network users still do not have the right tools to exploit these new advancements in AI, since they have to rely on low-level languages to specify network policies. Intent-based networking (IBN) allows operators to specify high-level policies that dictate how the network should behave without worrying how they are translated into configuration commands in the network devices. However, the existing research proposals for IBN fail to exploit the knowledge and feedback from the network operator to validate or improve the translation of intents. In this paper, we introduce a novel intent-refinement process that uses machine learning and feedback from the operator to translate the operator's utterances into network configurations. Our refinement process uses a sequence-to-sequence learning model to extract intents from natural language and the feedback from the operator to improve learning. The key insight of our process is an intermediate representation that resembles natural language that is suitable to collect feedback from the operator but is structured enough to facilitate precise translations. Our prototype interacts with a network operator using natural language and translates the operator input to the intermediate representation before translating to SDN rules. Our experimental results show that our process achieves a correlation coefficient squared (i.e., R-squared) of 0.99 for a dataset with 5000 entries and the operator feedback significantly improves the accuracy of our model., Comment: 9 pages, 5 figures, 3 listings, 1 grammar

This doctoral dissertation shows State of the Art of techno-economic modeling for access network technologies, presents the characteristics a universal techno-economic model should have, and shows a classification and analysis of techno-economic models in the literature based on such characteristics. In order to reduce the gap detected in the literature, the author defines and develops a Universal Techno-Economic Model called UTEM and the corresponding methodology to industrialize techno-economic assessment in multiple domains considering all market players perspectives, also suitable for technological consulting and currently available for all industry stakeholders under specific license of use., Comment: 188 pages, 30 figures, 56 tables, PhD Thesis, delivered in Dec. 2015, defended in Feb. 2016. Confidential chapters 3 and 4 excluded. English Translation. Original in Spanish available at University of the Basque Country official repository: https://addi.ehu.es/handle/10810/24669 *Total Spanish + English versions: 382 pages in arXiv: 2008.07286v3

Data representation plays a critical role in the performance of novelty detection (or ``anomaly detection'') methods in machine learning. The data representation of network traffic often determines the effectiveness of these models as much as the model itself. The wide range of novel events that network operators need to detect (e.g., attacks, malware, new applications, changes in traffic demands) introduces the possibility for a broad range of possible models and data representations. In each scenario, practitioners must spend significant effort extracting and engineering features that are most predictive for that situation or application. While anomaly detection is well-studied in computer networking, much existing work develops specific models that presume a particular representation -- often IPFIX/NetFlow. Yet, other representations may result in higher model accuracy, and the rise of programmable networks now makes it more practical to explore a broader range of representations. To facilitate such exploration, we develop a systematic framework, open-source toolkit, and public Python library that makes it both possible and easy to extract and generate features from network traffic and perform and end-to-end evaluation of these representations across most prevalent modern novelty detection models. We first develop and publicly release an open-source tool, an accompanying Python library (NetML), and end-to-end pipeline for novelty detection in network traffic. Second, we apply this tool to five different novelty detection problems in networking, across a range of scenarios from attack detection to novel device detection. Our findings general insights and guidelines concerning which features appear to be more appropriate for particular situations.

This article shows State of the Art of techno-economic modeling for access network technologies, presents the characteristics a universal techno-economic model should have, and shows a classification and analysis of techno-economic models in the literature based on such characteristics. As a result of his research in this direction, the author created and developed a Universal Techno-Economic Model and the corresponding methodology for techno-economic assessment in multiple domains, currently available for industry stakeholders under specific licence of use., Comment: 12 pages, 1 figure, 4 tables