The use of host intrusion detection systems shows promising results in detecting APT campaigns due to the use of systems logs as source data to get more information about system environment. However, dealing with the increase of logs in time while tracking the execution context is a challenge for security analysts. Therefore, this work presents backbone extraction as a crucial preprocessing step, filtering out irrelevant logs. As the logs are modeled as provenance graphs, we discard spurious edges to detect residuals with distinctive node and edge distributions that indicate security threats. By applying our methodology to state-of-the-art benchmark datasets, we observed an increase in the performance of one-class classifiers by up to 62% on F1-score and 48% on recall in the Streamspot dataset and by up to 40% on F1-score and 33% on recall in the DARPA3 THEIA dataset. Moreover, our results indicate mitigation of the dependency explosion problem and underscore the ability of our methodology to improve the detection landscape by shrinking graph sizes without losing essential aspects to characterize attacks.

Nowadays, the growth of privacy concerns imposes new requirements on security mechanisms deployed on autonomous vehicles. Assessing users’ misbehavior in Cooperative Intelligent Transport Systems (C-ITS) is crucial to keep them safe. Notwithstanding, data is a valuable asset, and exchanging them (e.g., Basic Safety Message—BSM) over the external network exposes sensitive data and compromises the privacy of CITS participants. This paper presents a federated learning-based architecture that shares model parameters for position spoofing detection in C-ITS. Our solution consists of buffering the host’s received messages and using them as predictors for misbehavior. To this end, we derived five novel features and group messages into analysis windows varying from 2 to 23 BSMs to predict dynamic attacker behavior better. Our results demonstrate the feasibility of training the models on the onboard unit (OBU) and sharing the models’ parameters in a federated learning-based architecture. Therefore, we bring users’ privacy to the table by preserving a local dataset, balancing the tradeoff between a rapid training process and reliable misbehavior detection. Moreover, our multilayer perceptron model outperforms the detecting position spoofing attacks in state-of-the-art works.

The Internet of Things (IoT) has undergone rapid popularization, reaching a wide range of application domains, such as manufactures. Hence, more and more heterogeneous IoT devices have been deployed in a variety of industrial environments, progressively becoming common objects to the supply chain. The physical infrastructure of manufacturing systems has become complex and requires efficient and dynamic solutions for managing network performance and security. Network Function Virtualization (NFV) has attracted attention when the intention is to respond to security threats on Industrial IoT (IIoT). Few works use NFV to detect and mitigate security threats on IIoT networks, but even less consider performance indicators of the network context when placing the Virtual Network Functions (VNFs). Thus, this work introduces a Machine Learning (ML) approach to place security VNFs based on NFV performance to mitigate Distributed Denial of Service (DDoS) attacks on IIoT. Experiments considering a new composed data set and diverse ML techniques show ML classification as an alternative for IIoT scenarios, achieving, according to the best-performing technique, 99.40% of accuracy in relation to the ideal placement. To facilitate the reproduction of the work, all the code and data produced are publicly available.

Recent advancements in autonomous vehicles and vehicular ad-hoc networks (VANETs) have presented diverse solutions for vehicle safety and automation. The demand to establish a connection between the two worlds has increased significantly to augment road safety and provide benefits to end users. Intelligent Transportation Systems (ITSs) vehicle station leaves a signed trace of its geographic location that is rated as personal data. An attacker can misuse existing V2V communication to track a vehicle’s CAM-trace and to avoid misusing CAMs to harm privacy, selectives communication approaches for CAMs should be chosen instead of continuous communication of current CAMs. In this article we propose a VANET topology learning methodology prioritizing anonymization that can use any existing Graph Learning framework. Further, this work enhances the quality of graph models applied to the context of VANETs and autonomous vehicles. With the real coordinates, establishment of a grid of cells and the generation and training of the graph, we can compare different frameworks generally used in these issues and one can chose the one that fits better to each scenario.

Basic Safety Messages (BSMs) are crucial for Cooperative Intelligent Transport Systems (C-ITS) to enable signalization of events and therefore allow vehicle synchronization to avoid accidents and improve traffic flows. However, in this context, entities can maliciously alter the content of BSMs and consequently incur disastrous events to disturb the CITS, causing user prejudice. This paper presents DISMISS-BSM, a novel misbehavior detection for detecting BSM forgery, consisting of buffering the host's received messages and using them as predictors. We compared our solution with state-of-the-art approaches, and the results indicate that our features promote better performance in identifying message forgery. We derive predictors considering the received signal strength and a movement pattern disruption indicator through our feature engineering process. Moreover, we use different sliding window lengths (2, 3, 8, 13, 18, and 23) to predict the dynamic attacker behavior better. Our results outperform the state-of-the-art and indicate that decision trees were the better conformant among K-NN, DT, MLP, and LSTM, performing the training phase in about 47 seconds on average for the signal strength and displacement compliance predictors.

With the growth in connected devices and network traffic, these systems require automated and fast approaches to achieve secure operations. Hence, machine learning-based network intrusion detection has become the state-of-the-art approach to tackle uncertainties and new attacks. However, the generalization of the models when exposed to different domains and workloads remains an open issue. In this paper, we propose using federated learning (FL) with sampling methods and feature selection to improve the generalization of the trained global model when evaluated in different network contexts. We evaluate this approach to classify network flows representing benign traffic and distributed denial-of-service attacks. Our proposed approach results in an 85% improvement compared with the naive evaluation of training in one context and evaluating others. Moreover, it presented a similar performance to a statistical algorithm with the reported generalization capability on flow-based network traffic classification. Additionally, this FL-based approach brings data privacy and distributed learning capability to the table.

The manual management of network assets is susceptible to configuration errors, lack of standardization, a large amount of repetitive work, and little or no traceability of changes over time. The Infrastructure as Code (IaC) approach makes it possible to automate the process of configuring resources such as operating systems, network services, containers, and applications, by treating them as software and allowing standardization and configuration rollback. This paper proposes an integrated architecture based on different software tools that use the IaC approach to automate the configuration of network assets, considering different models and manufacturers. A quantitative analysis shows the architecture’s efficiency in response time and scalability. The architecture has achieved a proportional gain of 83% in the average response time to manage 128 assets, with no significant increase in processing and memory usage.

In this paper, we propose a novel privacy-preserving identification-permission system (entitled Smart Human Identification System) to provide human identification, privacy, and security for users of wearables devices. To perform this function, we divided the system into six algorithms. We used PPG and ECG signals from two public datasets (MIMIC and CapnoBase). The proposed system creates a human ID and then compares it to other individuals and tests its accuracy to evaluate the quality of using biosignals for direct human identification in wearable devices. The experimental results indicate that the proposed system presented accuracy for the PPG signals from MIMIC and CapnoBase equal to 96.875% and 99.15%, respectively. For the ECG signals from MIMIC and CapnoBase, the accuracy obtained was 96.6% and 90.66%, respectively.

This work proposes FISHER: a deFense mechanIsm against timing-based Side-channel attack related to response time on the intERnet of things (IoT). IoT connects objects that support important applications, such as electronic health, smart homes, and Industry 4.0. However, timing-based side-channel attacks on IoT network traffic compromise user privacy. Related works present a limited view of side-channel leakages and as a solution, these works try to mask them. However, they ignore that devices have unique behaviors that intensify the problem of privacy leaks through response time. Hence, FISHER follows two modules: (i) vulnerability test and (ii) privacy protection. The vulnerability test module identifies timing-based side-channel leakages and reveals new vulnerabilities associated with the response time. The privacy protection module implements two methods that mask the identified time-based leakages on the network traffic. Results from an experimental scenario show that FISHER identifies precisely the side-channel leakages related to response time and efficiently masks them.

Critical urban events need to be efficiently handled, for instance, through rapid notification. VANETs are a promising choice in supporting notification of information on arbitrary critical events. Although the dynamicity of VANETs compromises the dissemination process, the connections among vehicles based on users' social interests allow for optimizing message exchange and data dissemination. This paper introduces SOCIABLE, a robust data dissemination system for critical urban events that operates in a SIoV network. It is based on vehicles' community with common interests and/or similar routines and employs social influence of vehicles according to their network location to select relay vehicles. In a comparative analysis on NS3 with the MINUET system, SOCIABLE achieved 36.56% less packets transmitted in a dense VANET and a maximum packet delivery delay of 28ms in a sparse VANET, delivering critical event data in a real-time and robust way without overloading the network.

Current IoT services include industry-oriented services, which often require objects to run more than one task. However, the exponential growth of objects in IoT poses the challenge of distributing and managing task allocation among objects. One of the main goals of task allocation is to improve the quality of information and maximize the tasks to be performed. Although there are approaches that optimize and manage the dynamics of nodes, not all consider the quality of information and the distributed allocation over the cluster service. This paper proposes the mechanism CONTASKI for task allocation in IIoT networks to distribute tasks among objects. It relies on collaborative consensus to allocate tasks and similarity capabilities to know which objects can play in accomplishing those tasks. CONTASKI was evaluated on NS-3 and achieved 100% of allocated tasks in cases with 75 and 100 nodes, and, on average, more than 80% clusters performed tasks in a low response time.

Wearable devices in e-Health provide easy usage access as well as an information return to the user. In general, such devices possess a range of sensors that capture several information from both the environment and the user. The most popular information collected by smartwatches and bracelets is about the measurement of heartbeats, steps, oxygenation, and photoplethysmogram (PPG) and electrocardiogram (ECG) signals. These wearable devices rely on mobile devices for user authentication. If the user needs validation, he will resort to traditional methods on other equipment that possesses recognition sensors such as iris, face, or fingerprints. In this paper, we introduce a model for double authentication based on PPG and ECG signals for promoting another layer of security to the user, ensuring data security, and avoid weak dependence on a single biosignal for validation. The proposed model has a algorithm with two zones, namely the Algorithm for PPG and ECG Signals and Error Rate zones. The experimental results indicate that the proposed model presented up to 99.98% of accuracy.

eHealth applications become popular with the increasing incidence of cancer and postoperative rehabilitation, that require continuous remote monitoring of patients. Given the huge diversity of eHealth applications, their proper and non- invasive identification assist in attaining important requirements as low latency and reliability. But, their identification is not trivial once they have similar characteristics to common applications. Also, the time taken to identify an eHealth application is crucial, however usually it is not addressed as relevant. This paper presents MOTIF, a method for identifying eHealth applications from side-channel information extracted from network traffic. It is non-invasive and does not inspect packet payload, employing machine learning algorithms for the particularities of healthcare scenarios. Results show MOTIF feasibility and point out an accuracy higher than 90% in less than 30 seconds.

Smart health (s-health) is a vital topic and an essential research field today, supporting the real-time monitoring of user’s data by using sensors, either in direct or indirect contact with the human body. Real-time monitoring promotes changes in healthcare from a reactive to a proactive paradigm, contributing to early detection, prevention, and long-term management of health conditions. Under these new conditions, continuous user authentication plays a key role in protecting data and access control, once it focuses on keeping track of a user’s identity throughout the system operation. Traditional user authentication systems cannot fulfill the security requirements of s-health, because they are limited, prone to security breaches, and require the user to frequently authenticate by, e.g., a password or fingerprint. This interrupts the normal use of the system, being highly inconvenient and not user friendly. Also, data transmission in current authentication systems relies on wireless technologies, which are susceptible to eavesdropping during the pairing stage. Biological signals, e.g., electrocardiogram (ECG) and electroencephalogram (EEG), can offer continuous and seamless authentication bolstered by exclusive characteristics from each individual. However, it is necessary to redesign current authentication systems to encompass biometric traits and new communication technologies that can jointly protect data and provide continuous authentication. Hence, this article presents a novel biosignal authentication system, in which the photoplethysmogram (PPG) biosignal and a galvanic coupling (GC) channel lead to continuous, seamless, and secure user authentication. Furthermore, this article contributes to a clear organization of the state of the art on biosignal-based continuous user authentication systems, assisting research studies in this field. The evaluation of the system feasibility presents accuracy in keeping data integrity and up to 98.66% accuracy in the authentication process.

In the near future, a significant increase of new services is estimated due to the advent of several heterogeneous devices connected to the Internet, with the capacity to exchange information, collect data and interact with the environment. Therefore, robust network infrastructures will be required to make these services available. The adoption of the Software Defined Networking (SDN) paradigm in Wireless Mesh Networks (WMN) fits in this context, opening up space for new features that optimize the network data flow, such as traffic engineering, flow-based packet forwarding, and interoperability. In this work, a performance analysis of OpenFlow is performed in a multi-interface WMN testbed. Among the experiments, we highlight the impact of heterogeneous mesh routers on the OpenFlow performance in single-channel and multi-channel scenarios, as well as the comparison of OpenFlow with three traditional WMN routing protocols: B.A.T.M.A.N., AODV, and HWMP. The results show a better performance of the OpenFlow in scenarios with homogeneous multi-channel routers in terms of throughput, delivery rate, jitter, and number of lost packets. In addition, the paper introduces problems related to the high overhead of OpenFlow in scenarios with multiple flows, as well as some directions to solve them.

Distributed Denial of Service (DDoS) attacks grow in volume, sophistication and impact. An example is the largest DDoS attack ever recorded against the developer platform GitHub, that reached 1.35 terabytes per second - an unprecedented volume of malicious traffic. DDoS attacks have been detected or mitigated only when they are in unrecoverable stages, being late to prevent their effects. Thus, differently from other works, we advocate for the early prediction of DDoS attacks to assist in reducing or avoiding costs and losses resulted from DDoS attacks. This paper presents STARK, a self- adaptable DDoS attack prediction system. STARK identifies signs of attack before it reaches an unrecoverable stage being founded on the metastability theory. Its evaluation follows a trace-driven approach, taking as input two databases containing records of DDoS attacks. Results show the prediction of DDoS attacks with minutes or hours in advance.

Heterogeneous wireless networks operate under un-certainties as traffic load variation and failures. Redundant packet scheduling algorithms seek to timely cope with these uncertainties to meet the latency and throughput requirements for delay-sensitive applications, such as online gaming, voice and video streaming. The Multipath Transmission Control Protocol (MPTCP) can rely on a redundant scheduler to fulfill this demand, replicating data packets on multiple paths to mitigate negative effects of heterogeneity, such as fluctuations in delay, loss rate, and bandwidth. However, shared bottlenecks among paths can compromise the benefits of the redundant scheduler and restrain the MPTCP performance. Thus, this paper introduces a new scheduler, called RED (REdundant Diversity scheduling), to prioritize packet replication by uncorrelated paths. RED selects paths and replicates packets based on the Spearman's correlation coefficient. Results show that RED achieves low delay and enhances the usage of low correlated paths, improving transmission performance.

In urban environments, several studies have shown that a mesh network is able to complement a number of existing access technologies, including wired broadband networks, 3G, 4G and commercial WiFi hotspots. However, due to the limited number of orthogonal channels available in the radio spectrum (3 in IEEE 802.11b / g / n in 2.4 GHz band), interference and containment bring new challenges to provide high performance in these networks. In this context, multi-channel multi-interface wireless mesh networks emerge as an efficient strategy of spreading the load through orthogonal channels to improve the network capacity. In this paper, we investigate different strategies for the use of multiple channels and multiple heterogeneous and homogeneous interfaces in a community area as a real university mesh network. Experimental results show that the use of multiple interfaces and multiple channels does not always increase the performance of the network in a real scenario.

The handoff process in heterogeneous wireless networks (HetNets) focuses on the maintenance of mobile users connectivity, seeking to keep them continuously connected to the best available network. By supporting a better user connectivity on HetNets, handoff decision methods should take into account in the choice procedure both QoS and security issues jointly with performance. Unfortunately, such methods have slightly addressed security by ignoring its properties, and such fragility can lead to risky set up for user's data confidentiality transmission. Particularly, handoff decision methods ignore the existing uncertainty in HetNets. But, uncertain information is common when handling wireless networks transition. Therefore, this work presents a method to support confidentiality-aware decision making in the mobile device handoff process by adapting the expected-utility theory. Simulation results show how this method brings advantages to the user connectivity providing both less risky choices and low handoff cost.

Recent works have sought to improve network performance by employing Multipath Transmission Control Protocol (MPTCP) to aggregate flows from heterogeneous wireless interfaces in a single connection. Although existing proposals are powerful, coupled congestion control algorithms suffer from high variation in path delays, bandwidth and loss rate, typical issues on heterogeneous wireless networks. Such variations are even higher over concurrent multipath transmissions, and they are enhanced in presence of bufferbloat, i.e. high delays caused by long queues. Hence, to cope with this limitation, we propose a major shift from the Active Queue Management (AQM) concept to Active Stack Management (ASM) concept, namely Controlled Delay ASM (CD-ASM) for HetNets to handle with the dropped packet ratio in the MPTCP congestion control mechanism. Differently from other approaches, CD-ASM gives priority to the most recent packets, being a promising solution. Moreover, we provide a detailed simulation analysis over congestion control algorithms by comparing CD-ASM to CoDel and DropTail schemes. Results indicate that our proposal reduces queue drops, and thus diminishing the impact on congestion control; keeping RTT low and improving in 20% the goodput.

Software Defined Networks (SDN) provide a high simplification of the network management by decoupling the control plane from the data plane through the use of controllers. Distributed-Denial-of-Service (DDoS) attacks can make SDN controllers unavailable to process legitimate flow requests from switches. The main approaches to protect controllers against DDoS attacks are essentially based on the attack detection, that still yield high rates of false negatives and/or false positives, highlighting the importance of mitigating DDoS attacks. Existing mitigation techniques are fundamentally based on external and additional resources or on the network traffic analysis, increasing computational cost or being prone to high rates of false negatives and/or false positives. This work presents PATMOS, a novel Protocol for DDoS Attack miTigation in Multi-contrOller SDN networkS through controller's clustering. PATMOS procedures are organized in three phases. The first one exchanges control messages to identify overloaded controllers, eliminating the dependence on the network traffic analysis. The second phase elects the best performance level controller to coordinate the mitigation process. The third phase minimizes the DDoS attacks effects using operational controllers in the network, differently from the works that employ external resources. Simulations results show PATMOS reducing 52.39% of CPU usage rate, increasing 192.74 fold more the throughput and decreasing 2.5 fold less the latency of flow requests to a target controller.

One of the main challenges on the next generation local wireless networks lies in providing scalable media access control protocols in order to support the predicted high density. Research results have indicated that the current backoff mechanism is ineffective for dense networks, even more under the foreseen growing charge for the next five years. This paper investigates the proposal of the contention window enlargement, particularly by the reduction of time slot. The proposal intends to be implemented in the the 802.11ax amendment. Our main contribution lies in highlighting the impact of this proposal under different classes of traffic, reflecting on the access categories for data priority and their respective window time. Simulation results indicate that the proposal mitigates collision issues inherent to the backoff procedure, and it decreases frame loss. This study shows improvements over throughput and retransmissions, and it also warns to the impact on the clear channel assessment process. Results contribute with the literature pointing out the advantages and disadvantages of the adaptive proposal considering different classes of traffic and promoting the advance of the next generation local wireless networks.

Identity management (IdM) systems employ Identity Providers (IdPs), as guardians of users' critical information. However, Distributed Denial-of-Service (DDoS) attacks can make IdPs operations unavailable, compromising legitimate users. In the literature, the main countermeasures against DDoS attacks are based on either the application of external resources to extend the system lifetime (replication) or on the DDoS attacks detection. The first approach increases the solutions cost, and in general the second one is prone to high rates of false negatives and/or false positives. This work presents SAMOS, a first scheme to mitigate DDoS attacks in IdM systems through a novel approach: organizations of IdP clustering using optimization techniques. SAMOS is started based on the monitoring of processing and memory resources, differently from the solutions in the literature that are started based on the attack detection by the network traffic analysis. SAMOS minimizes the DDoS attacks effects using operational IdPs in the system, differently from the works that employ external computer resources. Results considering data from real IdM systems indicate the scheme viability.

The Internet of Things (IoT) networks are vulnerable to various kinds of attacks, being the sinkhole attack one of the most destructive since it prevents communication among network devices. In general, existing solutions are not effective to provide protection and security against attacks sinkhole on IoT, and they also introduce high consumption of resources de memory, storage and processing. Further, they do not consider the impact of device mobility, which in essential in urban scenarios, like smart cities. This paper proposes an intrusion detection system, called INTI (Intrusion detection of SiNkhole attacks on 6LoWPAN for InterneT of ThIngs), to identify sinkhole attacks on the routing services in IoT. Moreover, INTI aims to mitigate adverse effects found in IDS that disturb its performance, like false positive and negative, as well as the high resource cost. The system combines watchdog, reputation and trust strategies for detection of attackers by analyzing the behavior of devices. Results show the INTI performance and its effectiveness in terms of attack detection rate, number of false positives and false negatives.

Real-time video dissemination over Vehicular Ad hoc Networks (VANETs) is fundamental for many services, e.g., emergency video delivery, road-side video surveillance, and advertisement broadcasting. These applications deal with several challenges due to strict video quality level requirements and highly dynamic topologies. To handle these challenges, geographic receiver-based beacon-less approaches have been proposed as a suitable solution for forwarding video flows in VANETs. In general, the routing decisions are performed only based on network, link, and/or node characteristics, such as link quality and vehicle's location. However, in real situations, due to different requirements and hierarchical structures of multimedia applications, these existent routing decisions are not satisfactory to select the best relay nodes and build up reliable backbones to delivery video content with reduced delay and high Quality of Experience (QoE). This paper introduces the QOe-Driven and LInk-qualiTy rEceiver-based (QOALITE) protocol to allow live video dissemination with QoE assurance in Vehicle-to-Vehicle (V2V) scenarios. QOALITE considers video and QoE-awareness, coupled with location and link quality attributes for relay selection. Simulation results show the benefits of QOALITE when compared to existing work, while achieving multimedia transmission with QoE support and robustness in highway scenarios.

The sensing of urban environments usually takes into account the deployment of a large number of devices to measure their environmental attributes, such as temperature, pressure, humidity, luminosity and pollution. In such applications, nearby sensors usually produce similar readings due to their spatial and temporal correlation. In the era of big data, management of collected data requires autonomous and scalable Wireless Sensor Network (WSN) structures. In this paper, we propose an in-network data storage model, called AQPM, that provides efficient processing of both spatial and value-based queries. AQPM is autonomous and scalable. That is, it does not rely on any central entity for neither managing data storage on sensor devices nor for processing queries. Scalability is achieved by grouping sensors with similar readings into clusters, while efficient query processing relies on the concept of repositories. Repositories are sensors that store readings of a set of clusters, and are the only ones that have to be contacted for answering queries. AQPM has been implemented on NS2 simulator and experimental results show that it is more effective than existing approaches.

Wireless Sensor Networks (WSNs) are an important interface between physical and computational environments, where nodes clustering is a common technique to organize its traffic. Although current clustering protocols are focused on various kinds of dynamicity on the network, such as mobility or cluster-head rotations, few solutions consider the readings similarity management, which would provide benefits in terms of better use of compression techniques and reactive detection of anomalous events. This paper proposes a dynamic clustering protocol, called DDFC, that manages spatial similarity between nodes readings. Its operation is based on the biological principles of fireflies to ensure distributed synchronization of the cluster's similar readings aggregations, differentiating thus from the classic use of fireflies. Simulations show that DDFC is capable of maintaining the cluster's readings aggregation synchronized, hence clustering nodes dynamically according to their similar readings.

Policies are used for developing adaptable and flexible systems in a variety of areas. They are especially suitable for reducing the complexity of managing tasks, by providing a mechanism for automatically tuning the system without human intervention. Policy-based systems have been applied for wireless sensor networks (WSNs) for controlling several functionalities. However, none of them has been proposed as a storage model, by making a clear distinction between storage functions and their behaviour. In this paper we propose SeSP, a Sensor Storage model based on Policies. SeSP explores concepts that are common in storage models proposed for WSNs in order to reduce the number of message transmissions and thus minimize the sensors' energy consumption. We have conducted a case study applying our policy-based system on two existing storage models: Scoop and DYSTO. Our experimental study, based on simulations, shows that SeSP can effectively reduce the number of transmissions, compared to the fixed values considered by both systems.

A reliable and robust routing service for Flying Ad-Hoc Networks (FANETs) must be able to adapt to topology changes. User experience on watching live video sequences must also be satisfactory even in scenarios with buffer overflow and high packet loss ratio. In this paper, we introduce a Cross-layer Link quality and Geographical-aware beaconless opportunistic routing protocol (XLinGO). It enhances the transmission of simultaneous multiple video flows over FANETs by creating and keeping reliable persistent multi-hop routes. XLinGO considers a set of cross-layer and human-related information for routing decisions, as performance metrics and Quality of Experience (QoE). Performance evaluation shows that XLinGO achieves multimedia dissemination with QoE support and robustness in a multi-hop, multi-flow, and mobile network environments.

Applications for Wireless sensor networks (WSN) usually take into consideration the specificity of the environment in which they are deployed in order to save the sensors' limited resources. In particular, the sensing task in urban environments requires hundreds and even thousands of sensors to be spread over the monitored area. Moreover, in environmental monitoring applications, sensors that are closely located usually provide similar readings. That is, spatial proximity is related to data similarity. In this paper we propose SIDS (Spatial Indexing Based on Data Similarity for Sensor Networks), a data model that explores this characteristic in order to provide scalability and efficient query processing on urban WSNs. Scalability is achieved by grouping sensors with similar readings, while efficiency for processing queries relies on two strategies: the concept of repositories, which consist of sensors that act as datacenters, and an indexing structure designed for speeding up both spatial and value-based queries. We have implemented the proposed model and results from simulations on a variety of scenarios show that SIDS provides scalability and it outperforms CAG and Peer-tree, which are models that have been proposed for processing data and spatial queries, respectively.

Network services in MANETs, such as resource location and distribution of connectivity information, deal with node mobility and resource constraints to support applications. The reliability and availability of these services can be assured by data management approaches, as replication techniques using quorum systems. However, these systems are vulnerable to selfish and malicious nodes, that intentionally do not collaborate with replication operations or spread malicious data while participating in data replication. In order to handle these issues, this paper proposes QS2, a bio-inspired scheme to tolerate selfish and malicious nodes in replication operation of quorum systems. Differently from existing works on the literature, QS2is distributed and self-organized, and each node has the autonomy to exclude misbehaving nodes. The scheme is inspired by quorum sensing and kin selection, both biological mechanisms resident in bacteria. Simulation results show that QS2improves significantly the reliability of a quorum system for MANETs, detecting more than 80% of misbehaving nodes on replication operations.

Due to the raising dependence of people on critical applications and wireless networks, high level of reliability, security and availability is claimed to assure secure and reliable service operation. Wireless ad hoc networks (WANETs) experience serious security issues even when solutions employ preventive or reactive security mechanisms. In order to support both network operations and security requirements of critical applications, we present SAMNAR, a Survivable Ad hoc and Mesh Network ARchitecture. Its goal lies in managing adaptively preventive, reactive and tolerant security mechanisms to provide essential services even under attacks, intrusions or failures. We use SAMNAR to design a path selection scheme for WANET routing. The evaluation of this path selection scheme considers scenarios using urban mesh network mobility with urban propagation models, and also random way point mobility with two-ray ground propagation models. Results show the survivability achieved on routing service under different conditions and attacks.

Mitigating Hot Spot energy consumption in Wireless Sensor Networks (WSNs) is a demanding task. Hot Spots have their nature bounded on routing - they are areas overloaded with high traffic rate, resulting in expanding energy holes. Hot Spots mitigation approaches have employed several techniques supported by routing protocols, such as multiple or mobile sinks, the deployment of more sensors in the Hot Spot area and unequal clustering. Albeit their advantages, cluster-based routing protocols to mitigate Hot Spots manage route maintenance inefficiently, leading to poor network performance and high energy consumption. This work presents an energy management approach to mitigate Hot Spots in WSN, supported by unequal clustering and low-costly dynamic route maintenance. We also generalize our solution for an energy management architecture that considers Hot Spot issues. Results show resilient routing and an efficient energy management, improving both network lifetime and performance.

Wireless mesh networks (WMNs) have emerged to support applications on various domains, such as military, financial and healthcare, that claim for high level of both security and performance. Since WMNs are susceptible to security issues, and their devices own limitations that can compromise network performance, this paper presents SECOM, a framework for self-configuration of WMNs to provide simultaneously security and performance. It comprises of different functional blocks to manage and adapt network services guaranteeing application requirements. We employ the framework on routing essential service, applying different security mechanisms and redundancies in network and link layers to assist route selection. Simulation results show that our framework enhances both security and performance, with low cost in terms of latency.

Wireless mesh networks (WMNs) have emerged as support for applications on various domains, such as military,financial and healthcare. Those applications claim for high level of both end-to-end performance and security. However, WMNs are naturally susceptible to security issues that can compromise network performance. This paper presents a cross-layer and adaptive scheme for balancing performance and security on data routing when the network experiences malicious actions from damaged nodes. Simulation results show that our approach based on a Multiple Criteria Decision Making method improves the tradeoff network performance and security even under malicious activity.

Abstract Computational grids are characterized by the collaborative work among environment devices. Further, grid applications have been built based on reputation solutions. However, those applications can suffer due to attacks from malicious nodes. These nodes may not only corrupt the result from processed jobs, but also intentionally acquire a good reputation so as to obtain privileges to damage other nodes. In order to detect and isolate these malicious nodes (known as intelligent cheating nodes) from a P2P grid computing, this work proposes a system‐level diagnosis model, using a strategy based on voting and honeypots. The model is evaluated by means of scenarios that take into account different percentages of malicious and cheating nodes. Achieved results show the robustness and efficiency of our diagnosis model, once all cheating nodes can be detected, with an accuracy of 99.4% of jobs being correctly processed. Furthermore, a graphical user interface is implemented for visualizing the simulations. Copyright © 2009 John Wiley & Sons, Ltd.

Cryptographic techniques are at the center of security solutions for wireless ad hoc networks. Public key infrastructures (PKIs) are essential for their efficient operation. However, the fully distributed organization of these networks makes a challenge to design PKIs. Moreover, changes in network paradigms and the increasing dependency on technology require more dependable, survivable and scalable PKIs. This paper presents a survivable PKI whose goal is to preserve key management operations even in face of attacks or intrusions. Our PKI is based on the adaptive cooperation among preventive, reactive and tolerant defense lines. It employs different evidences to prove the liability of users for their keys as well as social relationships for helping public key exchanges. Simulation results show the improvements achieved by our proposal in terms of effectiveness and survivability to different attacks.

Due to the emergence of multimedia context-rich applications and services over wireless networks, networking protocols and services are becoming more and more integrated, thus relying on context and application information to support their operation. Further, wireless protocols and services now employ information from several network layers and the environment, breaking the layering paradigm. In order to cope with this increasing reliance on information, we have proposed MANIP, a middleware for MANETs that instantiates a new networking plane. The Information Plane (InP) is a distributed entity to store and disseminate information concerning the network, its services and the environment, orchestrating the collaboration among cross-layer protocols, autonomic management solutions and context-aware services. We use MANIP to support the autonomic reconfiguration of a P2P network over MANETs. Simulation results show that the MANIP-enabled solutions reduce the response time and increase the number of solved P2P queries when compared to classic, cross-layer implementations of the same protocols.

Wireless mesh networks have gained increasing interests, but the lack of security guarantee has retarded their deployment. Security solutions have applied preventive or reactive mechanisms, being inefficient to put all attacks off. We design a survivable architecture for ad hoc and mesh networks to enhance the network capability of providing essential services even in face of attacks or intrusions. Our approach integrates preventive, reactive and tolerant defense lines in a self-adaptive way. Based on our architecture, we create a survival path selection scheme, and evaluate it through simulations using urban mesh network mobility and propagation models. Results show a decrease in the impact of routing attacks with minimal performance loss.

Many efforts have been made to develop security solutions for MANETs. These networks are vulnerable to diverse types of attacks, where routing is a critical operation. Current secure routing protocols are based on preventive or reactive security mechanisms, monitoring node behavior or controlling the access to networks and information. These mechanisms are insufficient to put all attacks and intrusions off, motivating the use of techniques that can tolerate them as multipath routing. This work proposes a new scheme based on fuzzy logic to select better paths and to increase network survivability. Our approach considers different selection criteria, where some of them represent network status and others are issued by preventive, reactive and tolerant defense lines. Simulations have been carried out comparing AODV and AOMDV with a modified AOMDV protocol. Results show the survivability of our approach under different conditions.

In mobile wireless ad hoc networks (MANETs), layering is frequently broken to cope with changes on the medium. Layering is also violated in order to implement autonomic behavior, which depends on correlating data from various layers to identify relevant events. This paper proposes a networking plane, called MANET knowledge plane (MANKOP), that stores information concerning all protocol layers. This plane improves network performance, as protocols may employ a broader range of inputs on their algorithms. Moreover, being an information repository, MANKOP eases the deployment of self-optimizing and self-configuring mechanisms. To showcase our solution, we use MANKOP to build transmission power control aware protocols, which increases the throughput of the network by up to 280%.

Malicious manipulation of jobs results endangers the efficiency and performance of grid computing applications. The presence of nodes interested in depreciating jobs results may be detected and minimized with the usage of fault tolerance techniques. In order to detect this kind of nodes, this paper presents a distributed and hierarchical diagnosis model based on comparison and reputation, which can be applied to both public and private grids. This strategy defines the status of a node according to its level of confidence, measured through its behavior. The proposed model was submitted to simulations to evaluate its effectiveness under different quota of malicious nodes. The results reveals that 8 test rounds can detect practically all malicious nodes and even with less rounds, the correctness remains high without a significant overhead increase

Wireless sensor networks are ad hoc networks with severe resource constraints. These constraints preclude the use of traditional ad hoc protocols, and demand optimizations that incur in solutions specific to a class of applications. This article presents PROC, a protocol designed for continuous data dissemination networks, that interacts with the application to establish routes, allowing the application to reconfigure PROC on runtime. A performance evaluation in topologies varying from 50 to 200 nodes showed that PROC increases network lifetime around 7% to 12%, and has higher throughput than EAD and TinyOS Beaconing. Furthermore, PROC presents a softer performance degradation when the number of nodes in the network increases.

Medium access control (MAC) protocols manage energy consumption on the network element during communication, which is the most energy-consuming event on Wireless Sensor Networks (WSNs). One method to mitigate energy consumption is to adjust transmission power. This paper presents two approaches to adjust transmission power in WSNs. The first approach employs dynamic adjustments by exchange of information among nodes, and the second one calculates the ideal transmission power according to signal attenuation in the link. The proposed algorithms were implemented and evaluated with experiments, comparing their results with B-MAC, the standard MAC protocol in the Mica Motes 2 platform. Results show that transmission power control is an effective method to decrease energy consumption, and incurs in a negligible loss in packet delivery rates. For node distances of 5m, the proposed transmission power control techniques decrease energy consumption by 27% over B-MAC.

It is often useful to examine management information base (MIB) objects of a faulty agent in order to determine why it is faulty. This paper presents a new framework for semi-active replication of SNMP management objects in local area networks. The framework is based on groups of agents that communicate with each other using reliable multicast. A group of agents provides fault-tolerant object functionality. An SNMP service is proposed that allows replicated MIB objects of a faulty agent of a given group to be accessed through fault-free agents of that group. The presented framework allows the dynamic definition of agent groups, and management objects to be replicated in each group. A practical fault-tolerant tool for local area network fault management was implemented and is presented. The system employs SNMP agents that interact with a group communication tool. As an example, we show how the examination of TCP-related objects of faulty agents have been used in the fault diagnosis process. The impact of replication on network performance is evaluated.