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Smart cities are based on complex systems that use legacy infrastructure and modern and intelligent solutions to achieve a highly efficient functioning of society in several verticals. In the context of road intersections, the main objective is to offer safe, efficient, and reliable mobility services for all agents involved in the physical space (infrastructure, vehicles, and pedestrians). The digital twin paradigm has recently been considered by both academia and industry as a means for reaching intelligent transportation solutions. The article makes a case for digital twins in intelligent intersection modeling as it is expected to consolidate real-time monitoring, control, and management of intelligent intersections. The design of a framework and the development of a testbed are presented in this paper. New functionalities for the safe operation of the road intersection are the expected results of this proposal.

Cyber-attacks cyber–physical systems (CPSs) can lead to sensing and actuation misbehavior, severe damages to physical objects, and safety risks. Machine learning algorithms have been proposed for hindering cyber-attacks on CPSs, but the absence of labeled data from novel attacks makes their detection quite challenging. In this context, generative adversarial networks (GANs) are a promising unsupervised approach to detect cyber-attacks by implicitly modeling the system. However, the detection of cyber-attacks on CPSs has strict latency requirements, since the attacks need to be stopped before the system is compromised. In this article, we propose FID-GAN, a novel fog-based, unsupervised intrusion detection system (IDS) for CPSs using GANs. The IDS is proposed for a fog architecture, which brings computation resources closer to the end nodes and thus contributes to meeting low-latency requirements. In order to achieve higher detection rates, the proposed architecture computes a reconstruction loss based on the reconstruction of data samples mapped to the latent space. Other works that follow a similar approach struggle with the time required to compute the reconstruction loss, which renders them impractical for latency constrained applications. We address this problem by training an encoder that accelerates the reconstruction loss computation. Experiments show that the proposed solution achieves higher detection rates and is at least 5.5 times faster than a baseline approach in the three studied data sets.

The presence of connected devices in homes introduces numerous threats to privacy via the analysis of the encrypted traffic these devices generate. Prior works have shown that traffic attributes such as packet size combined with machine learning techniques enable the inference of private information from Internet of Things users. One of the commonly used techniques to mitigate those privacy threats is traffic obfuscation, such as packet padding. Most padding mechanisms that were previously proposed statically select the number of bytes inserted in the packets, which incurs high overhead and ineffective privacy improvement. These static mechanisms are particularly unsuitable for networks whose traffic patterns are significantly dynamic, such as smart homes. This article proposes an adaptive packet padding approach based on software-defined networking (SDN) that adjusts the number of bytes inserted into packets in response to variations in the home network utilization. The proposed technique monitors the network to instruct a padding mechanism through a representational state transfer (REST) interface proposed in this article. This mechanism ensures that the length of packets generated by connected devices is modified. The evaluation includes four supervised learning mechanisms, random forest (RF), support vector machine (SVM), decision tree, and $k$ -nearest neighbors (KNNs), to measure privacy improvement through the metrics accuracy, recall, and $F1$ -score. Goodput, jitter, and packet loss induced by the proposal are also evaluated. Our proposal is shown to overcome the state-of-the-art solutions in privacy preservation with a significantly lower overhead. For instance, the accuracy of RF on identifying devices decreases from 96% to 4.96%.

Automotive Ethernet is being adopted in vehicles to provide the larger throughput that is required by autonomous vehicles. However, these vehicles may be subject to several cyber-attacks that compromise their operation and passengers' safety. This work proposes an Intrusion Detection System (IDS) that detects stream injection attacks on automotive Ethernet networks. The proposed IDS is based on feature generation and the XGBoost machine learning algorithm. Experimental results show that our proposed IDS achieves 0.9805 of AUCROC and a detection time of 620µs that allows real-time intrusion detection while using an inexpensive hardware platform, such as a Raspberry Pi. This is extremely important as cost is one of the automotive industry's main concerns.

The dynamic characteristics and unpredictability of the traffic generated by applications and services have given rise to some problems in data center networks (DCN). In particular, the concentration of elephant flows in DCNs can lead to a load imbalance because the equal-cost multi-path (ECMP) routing maps them to the same path. This can result in packet discard, which generates packet re-transmission, causes additional latency, and further degrades link performance. In this paper, we evaluate the performance of different elephant flow predictors with the use of real DCN traces. A traffic time series from a Facebook data center is used to model the elephant flow predictors on a short-term basis and to provide the descriptive statistics and inferences the flows. The paper proposes a hybrid prediction model by combining aspects of the FARIMA and the Recurrent Neural Network (FARIMA-RNN) models. A methodology based on the rank of the prediction accuracy metrics is applied to compare the performance of the hybrid model with the ARIMA, GARCH, RBF, MLP, and LSTM models. The comparison allows the selection of an appropriate model for the implementation of elephant flow scheduling techniques in DCNs. Results show that the FARIMA-RNN model presents lower error rates than the other predictors.

Elastic optical networks allow for a division of the optical spectrum into frequency slots, which can be combined to create channels with bandwidth defined by demand. Their flexibility provides greater spectral efficiency when compared with traditional optical networks with fixed grids. Traffic grooming strategies allow for even more efficiency in the use of network resources, reducing the number of guard bands and the utilization of transmitters and receivers. This work proposes a mechanism that influences the RMLSA algorithms causing a controlled level of spacing between the lightpaths. This makes it possible to expand lightpaths in the future to fulfill new customer requests through traffic grooming. The proposed mechanism does not require the use of specific traffic grooming or RMLSA algorithms, and it can be used in conjunction with existing solutions or future solutions. The experiments carried out show that the proposed mechanism reduces the bandwidth blocking ratio and the number of necessary transceivers, and also increases the request–lightpath ratio and energy efficiency in the network.

Recently, machine learning algorithms have been used to identify Internet of Things (IoT) devices and events. However, existing proposals may inspect the packet payload, what creates risks to IoT users’ privacy, and may use several features, increasing the computational complexity for traffic classification. In addition, existing techniques may also use complex mechanisms for extracting traffic characteristics, including the creation of vectors containing data from the Transmission Control Protocol (TCP) sessions. This paper proposes a solution that uses packet length statistics from encrypted traffic to characterize the behavior of IoT devices and events in a smart home scenario. The solution uses only the statistical mean, the standard deviation and the number of bytes transmitted over a one-second window, which can be extracted from the encrypted traffic, making the use of TCP vectors unnecessary. The solution identifies IoT devices and events, such as voice commands to smart assistants, and also distinguishes between IoT and non-IoT devices. The solution to characterize IoT devices and events is evaluated with traffic from two real-world testbeds and five classifiers. The evaluation included the algorithms k-Nearest Neighbors (k-NN), Decision Tree, Random Forest, Support Vector Machine (SVM) and Majority Voting, some of the most popular algorithms for traffic classification. The results show that the Random Forest algorithm can achieve up to 96% of accuracy in the identification of devices, 99% of precision in distinguishing between IoT and non-IoT devices and 99% of accuracy in the identification of IoT device events. Hypothesis testing is used to validate the obtained results. Also, the results show that the Decision Tree presented the lowest latency among the five classifiers evaluated in the identification of the devices, followed by k-NN, Random Forest, SVM and Majority Voting.

Routing, Modulation Level and Spectrum Assignment (RMLSA) é um dos principais problemas estudados nas redes ópticas elásticas. Este trabalho concentra-se no estudo da seleção da banda de guarda, um ou mais slots livres entre os circuitos, que é usada nas soluções do problema RMLSA. Neste contexto, uma nova abordagem, chamada de GUARDIAN, que usa uma rede neural multi layer perceptron para selecionar a banda de guarda de forma adaptativa é proposta. O desempenho da proposta é comparado com outras propostas adaptativas: AGBA e GBUN. A proposta alcança uma redução na probabilidade de bloqueio de banda de pelo menos 54,01% em relação ao AGBA e 51,26% em relação ao GBUN.

Recentemente, as redes ópticas elásticas com multiplexação por divisão espacial (SDM) apresentam-se como solução promissora para compor a infraestrutura da Internet. Este trabalho propõe o Algoritmo de Balanceamento de Núcleo e Espectro para Redes SDM (ABNE). Tal proposta busca uma alocação balanceada de núcleo e espectro, de modo a minimizar o impacto do crosstalk nos circuitos ópticos. A proposta é avaliada via simulação computacional e mostra-se eficiente em termos de probabilidade de bloqueio e eficiência energética quando comparada com outras soluções da literatura. O ABNE alcança uma redução na probabilidade de bloqueio de pelo menos 44% na topologia NSFNet e 33% na topologia EON.

The Controller Area Network (CAN) is the most pervasive in-vehiclenetwork technology in cars. However, since CAN was designed with no securityconcerns, solutions to mitigate cyber attacks on CAN networks have been pro-posed. Prior works have shown that detecting anomalies in the CAN networktraffic is a promising solution for increasing vehicle security. One of the mainchallenges in preventing a malicious CAN frame transmission is to be able todetect the anomaly before the end of the frame. This paper presents a real-timeanomaly-based Intrusion Detection System (IDS) capable of meeting this dead-line by using the Isolation Forest detection algorithm implemented in a hardwaredescription language. A true positive rate higher than 99% is achieved in testscenarios. The system requires less than 1μs to evaluate a frame’s payload, thusbeing able to detect the anomaly before the end of the frame.

Elastic optical networks are a network infrastructure capable of withstanding the high demand for data traffic from high-speed networks. One of the problems that must be solved to ensure the smooth functioning of the network is called Routing, Modulation Level and Spectrum Assignment (RMLSA). This work aims to propose a new approach to this problem with an algorithm to select the guard band in an adaptive way. Two algorithms for the adaptive selection of the guard band, called Guard Band according to Use of the Network (GBUN) and Guard Band by OSNR Margin (GBOM), are presented. The GBUN algorithm performs the guard band selection based on the usage level of network. On the other hand the GBOM algorithm uses an Optical Signal to Noise Ratio (OSNR) margin for the selection of the guard band. The performances of the proposed algorithms are compared with algorithms that use fixed guard band values and the adaptive proposal AGBA. The results showed that the GBOM algorithm presented a better performance in terms of bandwidth blocking probability for the studied scenarios. In general, GBOM also presents a better energy efficiency when compared to the other algorithms.

In this paper three algorithms are proposed that use the dedicated path protection strategy for survivable elastic optical networks. In addition to guarantee its availability upon the occurrence of a failure, proposed algorithms also seek to ensure the quality of the optical signal, which is degraded due to the physical layer effects. A performance evaluation was performed with dynamic traffic and requisitions with variable band demands. Simulation results show that taking into account physical layer effects in the choices of the routes of work and backup presented superior performance to other proposals of the literature.

Este artigo apresenta um estudo em redes ópticas elásticas considerando o problema de Power, Routing, Modulation Level and Spectrum Assignment (PRMLSA). O presente estudo se concentra na atribuição de potência por circuito. Nesse contexto, o algoritmo de Power Assignment by Binary Search (PABS), um novo algoritmo que realiza atribuição de potência por circuito de forma adaptativa, é proposto. O algoritmo PABS é comparado com outros algoritmos presentes na literatura em termos de probabilidade de bloqueio de banda. O algoritmo PABS consegue uma redução na probabilidade de bloqueio de banda de pelo menos 39% na topologia NSFNet e 42% na topologia Cost239. info:eu-repo/semantics/publishedVersion

This paper proposes a new Routing, Modulation Level and Spectrum Assignment (RMLSA) algorithm that considers the effects of physical layer in transparent elastic optical networks. The goal of the algorithm is to reduce the circuit blocking probability caused by the transmission quality degradation in the establishment of new circuits. The proposed algorithm is compared with two other Impairment-Aware RMLSA (IA-RMLSA) algorithms: Modified Dijkstra Path Computation (MD-PC) and K-Shortest Path Computation (KS-PC). The IA-RMLSA algorithms are evaluated under three different composition of bandwidth request for EON and NSFNet network topologies. Besides, we also evaluated the performance of IA-RMLSA algorithms using First-Fit and Best-Fit to spectrum assignment. Simulation results show that the proposed algorithm exhibits better performance than the MD-PC and KS-PC algorithms with regard to i) the circuit blocking probability, ii) the bandwidth blocking probability, iii) the fairness of service to different source-destination pairs, and iv) the fairness of service to different bandwidths. In general, regarding the circuit blocking probability, our algorithm achieved the best performance working with First-Fit (FF). Our algorithm using the FF has a minimum gain of 78.10% and 55.75% compared to the KS-PC-FF and MD-PC-FF algorithms, respectively, when the network attends 7 different values of bandwidth. Regarding the bandwidth blocking probability, the proposed algorithm using the FF has a minimum gain of 71.96% and 41.89% compared to the KS-PC-FF and MD-PC-FF algorithms, respectively. In general, regarding fairness, the proposed algorithm exhibits superior performance compared to the KS-PC and MD-PC algorithms.

As redes ópticas elásticas compõem uma infraestrutura de rede capaz de suportar a grande demanda de tráfego de dados das redes de alta velocidade. Um dos problemas que devem ser solucionados para garantir o bom funcionamento da rede é o chamado Routing, Modulation Level and Spectrum Allocation (RMLSA). Este trabalho tem como objetivo propor uma nova abordagem para esse problema por meio do uso de um algoritmo para provisão de banda de guarda adaptativa, baseado no nível de utilização da rede, chamado de GBUN. O desempenho do algoritmo proposto é comparado com algoritmos que usam valores de banda de guarda fixa e a proposta adaptativa AGBA. Os resultados mostram que o algoritmo proposto apresenta um melhor desempenho em termos de probabilidade de bloqueio de banda para os cenários estudados.

As redes ópticas elásticas permitem a divisão do espectro óptico em slots de frequência que podem ser combinados para criar canais com largura de banda definida sob demanda. Esta flexibilidade provê maior eficiência espectral em relação às tradicionais redes ópticas com grade fixa. Estratégias de agregação de tráfego permitem atingir uma eficiência ainda maior na utilização dos recursos da rede, reduzindo a quantidade de bandas de guarda e a utilização de transmissores e receptores. Este trabalho propõe um mecanismo que combinado às soluções de alocação de recurso já existentes, potencializa a agregação de tráfego. Os experimentos realizados mostram que a adoção do mecanismo proposto também contribui para a redução das taxas de bloqueio de banda sem comprometer o consumo energético.

The next generation of 5G networks is being developed to provide services with the highest Quality of Service (QoS) attributes, such as ultra-low latency, ultra-reliable communication, high data rates, and high user mobility experience. To this end, several new settings must be implemented in the mobile network architecture such as the incorporation of Network Function Virtualization (NFV) and Software-Defined Networking (SDN), along with the shift of processes to the edge of the network. This work proposes an architecture combining the NFV and SDN concepts to provide the logic for Quality of Service (QoS) traffic detection and the logic for QoS management in next-generation mobile networks. It can be applied to the mobile backhaul and the mobile core network to work with both 5G mobile access networks or current 4G access networks, keeping backward compatibility with current mobile devices. In order to manage traffic without QoS and with QoS requirements, this work incorporates Multiprotocol Label Switching (MPLS) in the mobile data plane. A new flexible and programmable method to detect traffic with QoS requirements is also proposed, along with an Evolved Packet System (EPS)-bearer/QoS-flow creation with QoS considering all elements in the path. These goals are achieved by using proactive and reactive path setup methods to route the traffic immediately and simultaneously process it in the search for QoS requirements. Finally, a prototype is presented to prove the benefits and the viability of the proposed concepts.

Over the last decade, passive optical network (PON) has been taken as the most promising next-generation access network. To improve the energy efficiency in PON systems, the ITU-T standard for next-generation PON system introduced a new power management mode, so-called the Watchful Sleep mode. This new mode is expected to be operated on any TDM-PON or GPON variant. Although the sleep mechanism under the Watchful Sleep mode is well defined, its performance can vary greatly depending on the criteria used to start and/or terminate the power saving phase. In this article, we present a comprehensive analytical model for evaluating the energy efficiency of the watchful sleep mode using the Delayed Wakeup (DWU) mechanism. We then compare its performance with the Immediate Wakeup (IWU) scheme. Our analytical and simulation results highlight the accuracy of the presented model and prove its merits.

In this paper, cryptographic schemes are applied to Ethernet-based layer-2 communication to provide authenticated encryption to safety- critical automotive control data. Confidentiality, integrity and authenticity are provided by combining AES with HMAC. Experimental results using low-cost hardware show that, despite the introduced cryptographic overhead, latency requirements are comfortably met for this type of communication.

In this paper we propose a circuit reallocation strategy for elastic optical networks aware of the effects of the physical layer. The proposed strategy aims to reorganize active circuits so that new circuit requests that are pending blockage are established. The strategy was applied with the algorithms CS, KSPC, KSP-RQoTO and MD-PC. The strategy was evaluate using the bandwidth blocking probability metric and components of the circuit blocking probability. The results were obtained using the real EON and NSFNet topologies. In terms of the probability of bandwidth blocking for the EON topology, for example, the reduction at the last load point was 81.01%, 89.2%, 80.93% and 78.27% for the CS, KS-PC, KSP-RQoTO and MD-PC algorithms, respectively. The blocking probability reduction for the NSFNet topology was 87%, 95.55%, 92.19% and 76.66%, for CS, KS-PC, KSP-RQoTO and MD-PC, also considering the last load point. The reductions in the probability of bandwidth blocking were due to the reallocations of circuits carried out by the strategy proposed in this work.

The continuously increasing consumption of power to access the Internet has been a major concern for network operators and equipment vendors. Passive optical network (PON) systems are widely seen as the future of broadband access. In 2010, ITU-T standardized a protocol-based PON energy efficiency mechanism that is comprised of two main modes, the doze mode and the cyclic sleep mode, which promise to save significant amounts of energy. However, the use of these two standardized alternative modes requires extra signaling and wastes energy. In this article we present the watchful sleep mode, a new mode that unifies the doze and cyclic sleep modes into a single power management mode. The new mode eliminates the extra control signaling and maximizes the amount of energy saved by keeping only the necessary hardware ON. Recently, the watchful sleep mode has been included in the ITU-T G.984 (G-PON) and ITUT G.987 (XG-PON) recommendations. It is expected to be operated as the only power management mode in future PON systems.

In this paper, a protection algorithm for elastic optical networks is proposed. The proposed algorithm takes into account the power consumption of the routes and the physical layer impairments to choose the working and protection routes. A performance evaluation study is carry out comparing the proposed algorithm with other algorithms presented in the literature. In general, the results show a good performance of the proposed algorithm in comparison to the other algorithms considered in terms of circuit blocking probability and power consumption.

Around the world, municipalities have been making substantial investments into broadband access infrastructure to accelerate the build-out of an urban phenomenon that has become known as a smart or connected city. At the 2016 Optical Fiber Communications Conference, a team contest, the Connected OFCity Challenge, was held to discuss the technological innovations and to examine dependencies and intricacies of a connected city project. The participants, four teams of experts coming from a cross-section of the industry, presented and defended their visions of future applications and innovative architecture and technologies to realize the interconnection. This paper provides a synthesis of the four competitive proposals offered for the contest and their ensuing discussions.

The existing ITU-T PON specifications define two distinct ONU power management modes: Cyclic Sleep and Doze. This paper proposes a parametric extension allowing the Unified power management mode, in which an ONU periodically turns off both its receiver and transmitter, as in the Cyclic Sleep mode, and performs infrequent bidirectional handshakes, as in the Doze mode. The timely reaction to an external stimulus is ensured by periodic unidirectional handshakes when only the ONU receiver is turned on. The Unified mode thus combines the advantages of the two standardized power saving modes, and a system that supports the Unified mode can emulate either the cyclic sleep or doze behavior as a special case. Simulation results show the energy efficiency superiority of the Unified mode over either of the two standardized modes in general.

This article provides an overview of current efforts in reducing energy consumption in passive optical access networks. Both ITU-T and IEEE standardized PONs are considered. The current solutions proposed by standardization authorities, industry, and academia are classified based on the layer they address in the standardized architectures: physical layer, data link layer, and hybrid. Then, the article provides answers to major questions, such as where, when, and how to reduce PON energy consumption in TDM PONs by means of a quantitative evaluation. Results show that to reduce energy consumption, ONUs must be provided with physical devices that are not only power-efficient but also provide improved services (e.g., fast synchronization) to upper layers. For this latter purpose, novel physical ONU architectures are proposed to speed up the synchronization process and enable effective data link layer solutions. Finally, the feasibility of switching ONUs to low power mode in idle slots is assessed through a testbed implementation.

Media streaming services are responsible for the significant increase of Internet traffic. This fact generates the need for better computing resources management in order to maintain such services always available. Cloud computing offers an elastic infrastructure that provides computing resources on demand in order to maintain such services always available. Moreover, emerging network architectures, such as Software Defined Networks, provide mechanisms that simplify the implementation of advanced network functions. In this context, this paper proposes an elastic approach to manage a video streaming service. The solution includes an OpenFlow-based load balancer, and an elasticity management mechanism that increases or decreases the amount of active streaming servers, considering customer's demand. A prototype was developed using a private cloud infrastructure and experiments were performed to show the viability and effectiveness of our solution, in terms of load balancing and elasticity functionality.