Summary Software defined network (SDN) has emerged as a new paradigm in terms of network architecture, providing flexibility, agility, and programmability to network management. These benefits boosted the SDN adoption, bringing new challenges mainly related to security, in particular, those related to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. The detection, prevention, and mitigation of these attacks are important since they can affect the entire network. Many current security measures use statistical techniques, as entropy, or machine learning (ML) algorithms to detect DoS and DDoS attacks. While the definition of a threshold to determine whether a traffic is an attack is not trivial in statistical techniques, ML solutions may provide better accuracy but require considerable computational resources and time to converge to a model able to detect these attacks. Trying to circumvent these limitations, current hybrid approaches either use the results from entropy as input in ML algorithms (EntropyML) or use entropy as a filter and ML algorithms to identify attacks. This work goes one step ahead and combines these techniques in a three‐step approach (EntropyMLEntropy), called ML‐Entropy, which inherits the intelligence of ML algorithms to adjust the threshold used by entropy. The proposed solution was implemented and evaluated in two datasets, the well‐known synthetic DARPA dataset and a dataset composed by traffic collected from a real‐corporate environment. Experimental results show that, in general, ML‐Entropy presents an accuracy above 99%, similar to support vector machine (SVC) and random forest (RF) algorithms, being able to converge to a detection model up to and faster than RF and SVC, respectively.

Summary The increasing demands for wireless channels to accommodate the surge of internet of things devices and the associated services exacerbated the need for flexible channel allocation strategies. Opportunistic spectrum sharing is expected to provide a more reasonable use of the limited radio frequencies by allowing the coexistence of licensed users and unlicensed users in the same frequency. This arrangement is called opportunistic channel allocation, where unlicensed users explore the channel when the licensed user is not transmitting. The challenge in opportunistic spectrum allocation is to find transmission opportunities. Statistical and machine learning techniques have been used to forecast spectrum opportunities on time slotted channels based on acquired transmission data from licensed users. However, opportunity forecast is usually limited to one slot ahead of time. This work explores the use of Hidden Markov Model (HMM) training and predicting procedures to forecast a consecutive sequence of idle slots that can be explored for opportunistic transmission. To improve prediction accuracy and speedup the training process, the proposed training procedures reduce the classification alphabet, which is achieved by balancing the number of training sequences to limit the influence of outliers and provide opportunity forecast even when the training process is executed over a limited number of observed sequences. While an aggressive predictor may explore more opportunities as compared to a conservative predictor, the former may incur in higher collision rates while the latter may explore fewer opportunities. Hence, this work proposes a metric to assist in the process of selecting the best predictor's parameters to balance collision and opportunity rates. The proposed HMM predictors are evaluated on both synthetic and real traffic traces. The results on synthetic traffic shows that, for channel load from up to , the proposed predictors identified over of channel opportunities with the collision rates below . On real traffic traces, where the licensed users' transmissions have deterministic inter‐packet delays, the HMM predictors identified over of the channel opportunities with collision rates below .

Summary Software defined network (SDN) is a paradigm that emphasizes the separation of the control plane from the data plane, offering advantages such as flexibility and programmability. However, from a security perspective, SDN also introduces new vulnerabilities due to the communication required between these planes. SYN Flood attacks are typical distributed denial‐of‐service (DDoS) attacks that especially challenge network administrators since they produce a large volume of semi‐open TCP connections to a target, compromising its availability. Most of the current solutions to detect and mitigate these attacks are designed to operate at the control plane, imposing an additional overhead on controller functions. Moreover, traffic‐blocking mechanisms, a widely used alternative to protect network resources, have the drawback of restricting legitimate traffic. This work proposes DataPlane‐ML, an integrated solution to detect and mitigate DDoS attacks on SDN, acting directly in the data plane. DataPlane‐ML uses machine learning techniques for attack detection and a mitigation solution based on the node's reputation to avoid blocking legitimate traffic during an attack. Experimental results show that DataPlane‐ML is faster than statistical‐based solutions for attack detection while presenting better accuracy. Moreover, the DataPlane‐ML mitigation solution can preserve more than of legitimate traffic during an attack.

Summary In a vehicular ad hoc network (VANET) each node is required to transmit periodic and event‐driven messages. The periodic messages are also known as beacons and consist of the basic vehicle status, such as position, speed, direction, and any additional information required by a safety application. One of the main concerns faced with this technology is channel congestion, which often happens in scenarios with a high vehicular density like traffic jams. When the channel is congested, there are no guarantees for a reliable message delivery. For this purpose, several protocols have been proposed to resolve this issue. In this work we propose neighbor congestion avoidance protocol (NCAP) as a novel solution employing a proactive strategy with vehicle position prediction using Kalman filter and an efficient event‐driven message delivery scheme. When compared with other strategies in congested scenarios, NCAP was able to significantly decrease the number of beacons by 49%, while also reducing the number of event‐driven message retransmissions by approximately 81%. NCAP also presented better performance in regard to the number of collisions between messages and channel busy ratio.

Vehicular networks have emerged as a viable option to disseminate information and prevent accidents in traffic environments. In these networks, vehicles can communicate with each other forming a Vehicular Ad-Hoc Network (VANET). In a VANET, vehicles do not require an external infrastructure to coordinate routing, they employ broadcast mechanisms to alert other drivers and pedestrians of possible risk situations. In this context, ensuring efficient message delivery is of utmost importance. However, the effect of multipath propagation can impair communication in VANETs since it introduces connection interruption areas that prevent message reception. This work proposes a solution to this problem employing a strategy called Coverage for a Vehicular Ad-Hoc Network (COVANET) that refines the way forwarders are chosen, thus ensuring neighbor awareness. Our proposal is simple enough that it can be easily adapted by other VANET protocols. The performance evaluation of COVANET was done through simulations and compared to other protocols, results show that our proposed approach can increase network coverage without incurring significant increases in latency, collisions or network load.

Static spectrum allocation policies allied with the increasing demand for higher data rates stimulated the pursuit of alternative spectrum allocation strategies. In this context, Opportunistic Spectrum Access (OSA) has been considered an alternative to allow licensed portions of the spectrum to be shared with unlicensed users. OSA requires unlicensed users to identify unused portions of the spectrum for opportunistic access that minimizes possible interference with the licensed users. Accurate mechanisms to avoid interference and improve the spectrum usage is highly desirable. This work investigates the performance of a traditional Hidden Markov Model (HMM) predictor where the licensed traffic follows a Poisson distribution. The results show that, under the evaluated settings, traditional HMM predictor can improve spectrum usage up to 15% at the expense of a high rate (≈ 50%) of inaccurate forecasts of idle periods. Based on these results, this paper proposes two techniques to optimize prediction performance and reduce inaccurate forecasts rate. Using the proposed enhancements inaccurate forecasts rate was reduced to only 6%. An additional benefit was observed when reducing collision with the licensed user, that is an improvement in the amount of slots effectively used by the unlicensed users from 15% to 23%.

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2018.

One of the major challenges in opportunistic networks is the correct identification of a transmission opportunity and its corresponding duration. In this work, a new adaptive model for opportunity forecast is proposed. The system is based on in-channel spectrum sensing and the use of recurrent neural network to model the occupation of the channel and detect the correct moment of transmission opportunity. The results, based on realistic experiments using a Software Defined Radio for monitoring a Wi-Fi channel, are presented. The proposed model reached a precision of 82.52% for noisy environment and 96.78% for mild environment, decreasing significantly the false positive rate comparing to non-adaptive recurrent neural equivalent, which is an important aspect in opportunistic use of a channel.

Full-duplex communications emerged as an alternative to improve throughput and spectrum usage on the forthcoming fifth-generation (5G) mobile networks. However, the existing medium access control schemes for full-duplex lack mechanisms designed to enhance spectrum usage. In this context, this work proposes FDDS-MAC, which is a full-duplex communication scheme tailored to improve spectrum usage. FDDS-MAC introduces a decision phase where the receiver with a higher probability of having data back to the sender is prioritized in the sender's packet queue. Thus, FDDS-MAC is able to perform packet rescheduling to boost throughput and spectrum usage. Numerical results show that FDDS-MAC has a positive impact over full-duplex communications, achieving throughput up to 40% higher than a state-of-art full-duplex scheme.

The increasing demands for high-data rate traffic stimulated the development of the fifth-generation (5G) mobile networks. The envisioned 5G networks is expected to meet its challenge by devising means to further improve spectrum usage. Full-duplex antennas and efficient medium access control (MAC) protocols are expected to play a major role in the foreseeable 5G networks. In this context, this paper presents a MAC layer technique to improve channel reservation time when full-duplex antennas are employed. Numerical results show that the proposed scheme provides throughput 156% higher than a state-of-the-art full-duplex protocol. When compared to traditional half-duplex antenna MAC protocols, throughput improvement of the proposed scheme reaches up to 412%.

The increasing demands for high‐data rate traffic stimulated the development of the fifth‐generation (5G) mobile networks. The envisioned 5G network is expected to meet its challenge by devising means to further improve spectrum usage. Many alternatives to enhance spectrum usage are being researched, such as massive MIMO, operation in mmWave frequency, cognitive radio, and the employment of full‐duplex antennas. Efficient utilization of the potential of any of these technologies faces a set of challenges related to medium access control (MAC) schemes. This work focuses on MAC schemes tailored for full‐duplex antennas, since they are expected to play a major role in the foreseeable 5G networks. In this context, this paper presents a MAC layer technique to improve total transmission time when full‐duplex antennas are employed. Several evaluations in different scenarios are conducted to assess the proposed MAC scheme. Numerical results show that the proposed scheme provides gains up to 156% when compared to a state‐of‐the‐art full‐duplex antenna MAC protocol. Compared to traditional half‐duplex antenna MAC protocols, the proposed scheme yields gain up to 412%.

Summary Digital halftoning is an important process to convert a grayscale image into a binary image with black and white pixels. Local exhaustive search‐based halftoning is one of the halftoning methods that can generate high‐quality binary images. However, considering the computing time, it is not realistic for most applications. As a first contribution, this paper proposes a graphics processing unit (GPU) implementation for digital halftoning employing local exhaustive search to produce high‐quality binary images. Programming issues of the GPU architecture have been carefully assessed for implementing the proposed method. Experimental results show that the proposed GPU implementation on NVIDIA (Santa Clara, CA, USA) GeForce GTX TITAN X attains a speed‐up factor of up to 48 over a CPU implementation. Our second contribution is a GPU implementation for cluster‐dot halftoning tailored for local exhaustive search. This implementation attains a speed‐up factor of 92 over a sequential CPU implementation. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Several important tasks, including matrix computation, signal processing, sorting, dynamic programming, encryption, and decryption, can be performed by oblivious sequential algorithms. A sequential algorithm is oblivious if an address accessed at each time does not depend on the input data. A bulk execution of a sequential algorithm is to execute it for many independent inputs in turn or in parallel. A number of works have been devoted to design and implement parallel algorithms for a single input. However, none of these works evaluated the bulk execution performance of these algorithms. The first contribution of this paper is to present a time‐optimal implementation for bulk execution of an oblivious sequential algorithm. Our second contribution is to develop a tool, named C2CU, which automatically generates a CUDA C program for a bulk execution of an oblivious sequential algorithm. The C2CU has been used to generate CUDA C programs for the bulk execution of the bitonic sorting, Floyd‐Warshall, and Montgomery modulo multiplication algorithms. Compared to a sequential implementation on a single CPU, the generated CUDA C programs for the above algorithms run, respectively, 199, 54, and 78 times faster.

Wireless ad hoc networks are often considered an alternative for providing communication means to support urgent and temporary tasks. In such settings, network connectivity is a primary condition to allow nodes to exchange information. In this work we explore the use of cooperative communication (CC) techniques for improving network connectivity. The proposed solution, named Connectivity Autorecovering with Cooperative Communication (CARCC), monitors potential network disruption points and proactively creates cooperative links (CC-links). These links are used to regain network connectivity when necessary. CARCC allows the selection of suitable CC-links in O(Δ(G)×Λ) time, whereas a centralized approach takes O(V2×Λ) time, where Δ(G) is the degree of the underline network graph G(V,E) and Λ is the computational cost of selecting helper nodes. Although CARCC relies on localized information, it is able to provide comparable results of other noteworthy alternatives that require global topological information. The CC-links provided by CARCC reestablished network connectivity in over 91% in the evaluated settings.

Wireless Sensor Networks (WSN) are characterized by their capacity of monitoring the environment, gathering and sharing information. Nodes in a WSN usually cooperate in the task of forwarding the sensed data to a sink node for later retrieval and analysis. The success of this task depends on the availability of efficient routes that meet the application requirements. As topology may change overtime, Cooperative Communication (CC) techniques have been explored to improve network connectivity. However, CC links are known to have higher energy demands as compared to traditional links. This paper presents a proposal of a new cooperative communication model capable of increasing energy balance of the CC-links while improving network connectivity. Simulation results show that, compared to other CC schemes, the source node of a cooperative communication reduces the amount of expanded energy by over 70% in the evaluated settings.

One of the major challenges in opportunistic networks is the correct identification of a transmission opportunity and its corresponding duration. In this work, recurrent neural network structures are investigated for transmission opportunity forecast. The proposed method is based on in-channel spectrum sensing and the use of Elman recurrent neural network to model the occupation of the channel. The results, based on real experiment measurements, using a Software Defined Radio for monitoring of a Wi-Fi channel shows that, in the evaluated scenarios even a small neural network is able to achieves 99.9% of correct transmission opportunity identification rate.

The sensor nodes in a Wireless Sensor Networks (WSNs) are responsible to collect and share the sensed information, usually by making it available at a gateway point. In this context, network connectivity plays an important role as the nodes require the availability of a path to route the sensed data to the sink node. The main contribution of this work is to propose a cooperative communication strategy aiming at improving network connectivity. To attain this goal, the proposed technique, called Sink-Path Cooperative Communication (Sink-PathCC) provide means to reduce the number of hops to reach the sink node while improving energy balance of the nodes employing cooperative communication. Simulation results show that Sink-PathCC provides better energy balance as compared to other similar proposals. In addition, Sink-PathCC is able to reduce the power consumption of the source node by nearly 60% in the evaluated scenarios.

The use of directional antennas in support for ad hoc communications has increased significantly since these antennas provide throughput gain and increase space division. Most of the proposed directional MAC protocols in the literature rely on the use of control frames - such as RTS/CTS - for channel reservation and to reduce the collision probability of data frames. However, RTS/CTS control frames have a high cost as they increase latency and end-to-end delay. The main contribution of this paper is to propose a channel reservation technique based on pulse/tone signals for a model that considers single radio and single channel. Called directional pulse/tone channel reservation (DPTCR), this technique reduces the latency and has a low probability of failure. DPTCR has been validated through a theoretical analysis followed by simulations. The results show that DPTCR is able to provide throughput gains up to 40.6% higher as compared to traditional channel reservation schemes employing RTS/CTS frames.

The popularization of wireless technologies, combined with applications that require continuous connection and high transmission rates, has driven the quest for energy-efficient Medium Access Control (MAC) protocols. Means to improve network performance by exploring the availability of multiple communication channels has been explored in the literature. However, devising energy-efficient protocols, capable to perform the tasks of channel assignment and communication scheduling while improving network performance has been shown to be a challenging task. In this context, this paper proposes a channel allocation and communication scheduling protocol for wireless networks that minimizes both power consumption and communication time. The proposed protocol allows to maintain power consumption at minimum while being efficient in terms of communication time.

Due to high demands of accuracy and efficiency while dealing with lots of complex and ambiguous gestures, the sign language recognition is still a hard problem to vision-based systems. From depth images acquired by a recent RGB-D sensor, this paper proposes a novel methodology for the recognition of the American Sign Language hand alphabet, which is underlain by a detailed analysis of the Iterative Closest Point (ICP) algorithm, applied here as a tridimensional shape matching procedure. The evaluation of the inputs and outputs of the ICP technique combined with the interpretation of the conducted experiments contribute significantly for advancing the state of the art, by identifying in which conditions the alignment can be used in compliance with the requirements of the pattern recognition.

Wireless networking enhanced with multiple transmitting channels has been considered to improve the utilization of the electromagnetic spectrum and to provide ways to accommodation the growing demand for connectivity. In this scenario, the use of a common control channel to coordinate the stations and networking resources is usually employed. This work evaluates the control channel capacity and its impact on the overall system throughput when multiple channels are available. The CSMA/CA mechanism is considered as it is the most used access control mechanism in the wireless local area network. To this end, an analytical model is proposed to evaluate the saturation conditions of the control channel. Empirical results are also provided and compared with the analytical model. The results show that the empirical results are consistent with the analytical model. In particular, it is shown that the ratio of eight data channels per control channel attains the best results.

The IEEE802.11 standard has been commonly employed in ad hoc networks. The use of directional antennas in such networks has increased significantly since these antennas provide throughput gain and increase space division. However, IEEE802.11 based directional MAC protocols have a number of challenges to overcome. Latency due to channel reservation using frames RTS/CTS is one of them. In this context, this paper proposes a channel reservation technique based on pulse/tone signals. This technique reduces the latency and has a very low probability of failure. The proposed technique has been validated through a theoretical analysis followed by simulations. The results show that the proposed technique is able to provide a throughput up to 42.9% higher than when RTS/CTS scheme is used.

The radio spectrum is a finite resource and the current spectrum licensing model has led to a fenomenon known as spectrum scarcity. Several approaches have been presented in order to improve the effectiveness of spectrum utilization. This works main contribution is is to present an approach to opportunistic spectrum sensing and allocation focused on maximizing spectrum usage while minimizing communication time and interference on primary communications. Compared an alternative, our approach presents significant improvements in the spectrum access and utilization, improving network throughput up to 16 times.

The growing demand for mobile wireless access has stimulated the emergence of new communication technologies. Opportunistic Spectrum Access (OSA) is viewed as a promising alternative to overcome the problems caused by static spectrum assignment. Opportunistic access allows dynamic mapping of the transmission needs and communication opportunities. However, performing this task efficiently is not trivial. Indeed, it has been shown to be NP-complete. In this context, this paper presents an efficient heuristic for solving the channel allocation and data assignment problem, according to the opportunities and channels available. The proposed heuristic is optimal in terms of energy consumption, being close to the optimum, about 5% above, in terms of transmission time.

Multiple sequence alignment (MSA) is an important problem in Bioinformatics since it is often used to identify evolutionary relationships and predict secondary/tertiary structure, among others. MSAs are usually scored with the Sum-of-Pairs (SP) function and the exact SP MSA is known to be NP-Hard. Therefore, heuristic methods are used to solve this problem. In this paper, we propose and evaluate a parallel island injection genetic algorithm to solve the MSA problem. Unlike the other strategies, our parallel solution uses two types of interconnected archipelagoes, each with distinct types of individuals. Our results with real protein data sets show that our strategy is able to obtain better results, when compared to the traditional island model. Also, we were able to reduce considerably the execution time, when compared to the sequential version.

The main contribution of this work is to propose a distributed on-demand routing and wavelength assignment algorithm for WDM networks. The proposed algorithm, termed WDM-DSR, is capable to select routes and establish light-paths via message exchanges without imposing a major overhead on the network. Also, we show that the proposed scheme can be used to balance the load in a WDM network. The simulation results show that the proposed solution is comparable with the other algorithms that demands for a much higher computational and message costs.

The main contribution of this article is to propose a collaborative and distributed cache mechanism tailored for ad hoc networks. Each node's cache is partitioned into a shared area and a private area. The proposed collaborative cache is formed by each node's shared area, which is used to store contents relevant to the group, while the private area is used to store information relevant to its owner. Empirical results have shown that our collaborative cache mechanism can significantly reduce the amount of network traffic, latency and energy consumption as well as the server's load.

Access networks based on cooper cables are costly to build and maintain. For this reason, last-mile access networks based on wireless technologies are gaining considerable attention. Among the wireless technologies being employed, the IEEE802.11 is common place. In such scenarios it is important to have well defined mechanisms to better evaluate traffic characteristics and overall system performance. Such understanding can help network designers to better estimate the resources needed to provide basic services with a reasonable level of quality (QoS). This task, however, has been shown to be non-trivial. The main contribution of this work is to present techniques than can be applied to estimate the throughput and the access pattern for basic services in the context of the IEEE802.11 based networks.

Channel allocation has been considered a NP-complete problem, and involves a very important issue for the adequate dimensioning of wireless communication networks. One of the possibilities for its solution involves the utilization of heuristics, able to produce near optimal solutions in an acceptable computational time. In this paper, a hybrid of GRASP (greedy randomized adaptive search procedure) and FEA (frequency exhaustive assignment) is proposed, allowing the achievement of best solutions for the channel allocation problem (CAP).

We address the demodulation and decoding of array signals in the presence of fluctuating cochannel interference (CCI). The maximum-likelihood decoder is formulated for the case of N receive antennas and stationary, temporally white Gaussian CCI within blocks of K samples, where KgesN. The pair wise error probability (PEP) is utilized to quantify the degradation in performance resulting from the absence of side information about the CCI. For pairs of code words that differ over a large number of blocks, an asymptotic PEP performance degradation of 10log10(K/(K-N)) decibels is found. An iterative maximization of the log-likelihood function leads to an alternative suboptimal receiver with reduced complexity, including a block-wise adaptive beamforming via a sample matrix inversion (SMI) and a nearest-neighbor decoding. From the statistics of the spatially filtered signals we find the asymptotic PEP performance, and see that the presence of the desired signal, which is known to adversely affect the output of SMI-based beamformers with small sample support, is gradually mitigated in the iterative procedure

Despite the efforts to leverage the network performance through the use of Directional Communications, most protocols still rely on the basic mechanisms employed by the IEEE802.11 standards. While this might be desirable to facilitate compatibility, nodes empowered with directional antennas should be able to explore the benefits of directional communications whenever possible. With that in mind, we propose two mechanisms, termed Adaptive Carrier Sensing and Adaptive Packet Sending, aiming to allow nodes equipped with directional antennas to exploit the advantages of such systems to improve network performance.

The main contribution of this work is to present energyefficient protocols that compute the sum of n numbers over any commutative and associative binary operator stored in n wireless sensor nodes arranged in a two-dimensional grid # n. We first present a protocol that computes the sum in O(r 3 ) time slots with no sensor node being awake for more than O(1) time slots, where r is the transmission range of the sensor nodes. We then show a fault-tolerant protocol that computes the sum in the same number of time slots with no sensor node being awake for more than O(log r) time slots.