Dense Multi-Channel Sniffing in Large IoT Networks

International audienceIn this article we deal with the issue of network traffic monitoring in large multi-channel wireless IoT networks. Assuming known link conditions on all radio channels, i.e. connectivity matrix defined through Packet Delivery Ratio on all frequencies and all links between nodes, we propose two methods for defining the number and the positions of sniffer devices, with the goal to maximize the capture of network traffic. Method I is based on probabilistic theory and assumes brute-force search over the connectivity matrix for defining the optimal positions of a given number of sniffers, or, for a given percentage of the traffic to be captured as the input parameter, this method determines number of sniffers and their locations. Due to the computational complexity of brute-force search of the connectivity matrix, we complement Method I and propose Method II. Method II is based on graph theory and uses the minimal Packet Delivery Ratio on each link as the input parameter for defining the number and position of sniffers. We input traffic traces from an experimental testbed into the network to examine and compare both methods. Results show that the Method I outperforms Method II in the percentage of captured network traffic, for a given number of deployed sniffers. However, Method II complements Method I in scenarios where there are a large number of sniffers, due to lower computational complexity

Prediction-based techniques for the optimization of mobile networks

Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition of several random phenomena, most of which, related to human activities, such as mobility, communications and network usage. However, when observed in their totality, the many individual components merge into more deterministic patterns and trends start to be identifiable and predictable. In this thesis we analyze a recent branch of network optimization that is commonly referred to as anticipatory networking and that entails the combination of prediction solutions and network optimization schemes. The main intuition behind anticipatory networking is that knowing in advance what is going on in the network can help understanding potentially severe problems and mitigate their impact by applying solution when they are still in their initial states. Conversely, network forecast might also indicate a future improvement in the overall network condition (i.e. load reduction or better signal quality reported from users). In such a case, resources can be assigned more sparingly requiring users to rely on buffered information while waiting for the better condition when it will be more convenient to grant more resources. In the beginning of this thesis we will survey the current anticipatory networking panorama and the many prediction and optimization solutions proposed so far. In the main body of the work, we will propose our novel solutions to the problem, the tools and methodologies we designed to evaluate them and to perform a real world evaluation of our schemes. By the end of this work it will be clear that not only is anticipatory networking a very promising theoretical framework, but also that it is feasible and it can deliver substantial benefit to current and next generation mobile networks. In fact, with both our theoretical and practical results we show evidences that more than one third of the resources can be saved and even larger gain can be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi

Security of Software-defined Wireless Sensor Networks

Wireless Sensor Network (WSN) using Software Defined Networking (SDN) can achieve several advantages such as flexible and centralized network management and efficient routing. This is because SDN is a logically centralized architecture that separates the control plane from the data plane. SDN can provide security solutions, such as routing isolation, while handling the heterogeneity, scalability, and the limited resources of WSNs. However, such centralized architecture brings new challenges due to the single attack point and having non-dedicated channels for the control plane in WSNs. In this thesis, we investigate and propose security solutions for software-defined WSNs considering energy-efficiency and resource-preservation. The details are as follows. First, the functionality of software-defined WSNs can be affected by malicious sensor nodes that perform arbitrary actions such as message dropping or flooding. The malicious nodes can degrade the availability of the network due to in-band communications and the inherent lack of secure channels in software-defined WSNs. Therefore, we design a hierarchical trust management scheme for software-defined WSNs (namely TSW) to detect potential threats inside software-defined WSNs while promoting node cooperation and supporting decision-making in the forwarding process. The TSW scheme evaluates the trustworthiness of involved nodes and enables the detection of malicious behavior at various levels of the software-defined WSN architecture. We develop sensitive trust computational models to detect several malicious attacks. Furthermore, we propose separate trust scores and parameters for control and data traffic, respectively, to enhance the detection performance against attacks directed at the crucial traffic of the control plane. Additionally, we develop an acknowledgment-based trust recording mechanism by exploiting some built-in SDN control messages. To ensure the resilience and honesty of the trust scores, a weighted averaging approach is adopted, and a reliability trust metric is also defined. Through extensive analyses and numerical simulations, we demonstrate that TSW is efficient in detecting malicious nodes that launch several communication and trust management threats such as black-hole, selective forwarding, denial of service, bad and good mouthing, and ON-OFF attacks. Second, network topology obfuscation is generally considered a proactive mechanism for mitigating traffic analysis attacks. The main challenge is to strike a balance among energy consumption, reliable routing, and security levels due to resource constraints in sensor nodes. Furthermore, software-defined WSNs are more vulnerable to traffic analysis attacks due to the uncovered pattern of control traffic between the controller and the nodes. As a result, we propose a new energy-aware network topology obfuscation mechanism, which maximizes the attack costs and is efficient and practical to be deployed. Specifically, first, a route obfuscation method is proposed by utilizing ranking-based route mutation, based on four different critical criteria: route overlapping, energy consumption, link costs, and node reliability. Then, a sink node obfuscation method is introduced by selecting several fake sink nodes that are indistinguishable from actual sink nodes, according to the k-anonymity model. As a result, the most suitable routes and sink nodes can be selected, and a highest obfuscation level can be reached without sacrificing energy efficiency. Finally, extensive simulation results demonstrate that the proposed methods strongly mitigate traffic analysis attacks and achieve effective network topology obfuscation for software-defined WSNs. In addition, the proposed methods reduce the success rate of the attacks while achieving lower energy consumption and longer network lifetime. Last, security networking functions, such as trust management and Intrusion Detection System (IDS), are deployed in WSNs to protect the network from multiple attacks. However, there are many resource and security challenges in deploying these functions. First, they consume tremendous nodes’ energy and computational resources, which are limited in WSNs. Another challenge is preserving the security at a sufficient level in terms of reliability and coverage. Watchdog nodes, as one of the main components in trust management, overhear and monitor other nodes in the network. Accordingly, a secure and energy-aware watchdog placement optimization solution is studied for software-defined WSNs. The solution balances the required energy consumption, computational resource, and security in terms of the honesty of the watchdog nodes. To this end, a multi-population genetic algorithm is proposed for the optimal placement of the watchdog function in the network given the comprehensive aspects of resources and security. Finally, simulation results demonstrate that the proposed solution robustly preserves security levels and achieves energy-efficient deployment. In summary, reactive and proactive security solutions are investigated, designed, and evaluated for software-defined WSNs. The novelty of these proposed solutions is not only efficient and robust security but also their energy awareness, which allows them to be practical on resource-constrained networks. Thus, this thesis is considered a significant advancement toward more trustworthy and dependable software-defined WSNs

Bayesian Learning Strategies in Wireless Networks

This thesis collects the research works I performed as a Ph.D. candidate, where the common thread running through all the works is Bayesian reasoning with applications in wireless networks. The pivotal role in Bayesian reasoning is inference: reasoning about what we don’t know, given what we know. When we make inference about the nature of the world, then we learn new features about the environment within which the agent gains experience, as this is what allows us to benefit from the gathered information, thus adapting to new conditions. As we leverage the gathered information, our belief about the environment should change to reflect our improved knowledge. This thesis focuses on the probabilistic aspects of information processing with applications to the following topics: Machine learning based network analysis using millimeter-wave narrow-band energy traces; Bayesian forecasting and anomaly detection in vehicular monitoring networks; Online power management strategies for energy harvesting mobile networks; Beam training and data transmission optimization in millimeter-wave vehicular networks. In these research works, we deal with pattern recognition aspects in real-world data via supervised/unsupervised learning methods (classification, forecasting and anomaly detection, multi-step ahead prediction via kernel methods). Finally, the mathematical framework of Markov Decision Processes (MDPs), which also serves as the basis for reinforcement learning, is introduced, where Partially Observable MDPs use the notion of belief to make decisions about the state of the world in millimeter-wave vehicular networks. The goal of this thesis is to investigate the considerable potential of inference from insightful perspectives, detailing the mathematical framework and how Bayesian reasoning conveniently adapts to various research domains in wireless networks

Evaluating Machine Learning Techniques for Smart Home Device Classification

Smart devices in the Internet of Things (IoT) have transformed the management of personal and industrial spaces. Leveraging inexpensive computing, smart devices enable remote sensing and automated control over a diverse range of processes. Even as IoT devices provide numerous benefits, it is vital that their emerging security implications are studied. IoT device design typically focuses on cost efficiency and time to market, leading to limited built-in encryption, questionable supply chains, and poor data security. In a 2017 report, the United States Government Accountability Office recommended that the Department of Defense investigate the risks IoT devices pose to operations security, information leakage, and endangerment of senior leaders [1]. Recent research has shown that it is possible to model a subject’s pattern-of-life through data leakage from Bluetooth Low Energy (BLE) and Wi-Fi smart home devices [2]. A key step in establishing pattern-of-life is the identification of the device types within the smart home. Device type is defined as the functional purpose of the IoT device, e.g., camera, lock, and plug. This research hypothesizes that machine learning algorithms can be used to accurately perform classification of smart home devices. To test this hypothesis, a Smart Home Environment (SHE) is built using a variety of commercially-available BLE and Wi-Fi devices. SHE produces actual smart device traffic that is used to create a dataset for machine learning classification. Six device types are included in SHE: door sensors, locks, and temperature sensors using BLE, and smart bulbs, cameras, and smart plugs using Wi-Fi. In addition, a device classification pipeline (DCP) is designed to collect and preprocess the wireless traffic, extract features, and produce tuned models for testing. K-nearest neighbors (KNN), linear discriminant analysis (LDA), and random forests (RF) classifiers are built and tuned for experimental testing. During this experiment, the classifiers are tested on their ability to distinguish device types in a multiclass classification scheme. Classifier performance is evaluated using the Matthews correlation coefficient (MCC), mean recall, and mean precision metrics. Using all available features, the classifier with the best overall performance is the KNN classifier. The KNN classifier was able to identify BLE device types with an MCC of 0.55, a mean precision of 54%, and a mean recall of 64%, and Wi-Fi device types with an MCC of 0.71, a mean precision of 81%, and a mean recall of 81%. Experimental results provide support towards the hypothesis that machine learning can classify IoT device types to a high level of performance, but more work is necessary to build a more robust classifier

Optimizing the delivery of multimedia over mobile networks

Mención Internacional en el título de doctorThe consumption of multimedia content is moving from a residential environment to mobile phones. Mobile data traffic, driven mostly by video demand, is increasing rapidly and wireless spectrum is becoming a more and more scarce resource. This makes it highly important to operate mobile networks efficiently. To tackle this, recent developments in anticipatory networking schemes make it possible to to predict the future capacity of mobile devices and optimize the allocation of the limited wireless resources. Further, optimizing Quality of Experience—smooth, quick, and high quality playback—is more difficult in the mobile setting, due to the highly dynamic nature of wireless links. A key requirement for achieving, both anticipatory networking schemes and QoE optimization, is estimating the available bandwidth of mobile devices. Ideally, this should be done quickly and with low overhead. In summary, we propose a series of improvements to the delivery of multimedia over mobile networks. We do so, be identifying inefficiencies in the interconnection of mobile operators with the servers hosting content, propose an algorithm to opportunistically create frequent capacity estimations suitable for use in resource optimization solutions and finally propose another algorithm able to estimate the bandwidth class of a device based on minimal traffic in order to identify the ideal streaming quality its connection may support before commencing playback. The main body of this thesis proposes two lightweight algorithms designed to provide bandwidth estimations under the high constraints of the mobile environment, such as and most notably the usually very limited traffic quota. To do so, we begin with providing a thorough overview of the communication path between a content server and a mobile device. We continue with analysing how accurate smartphone measurements can be and also go in depth identifying the various artifacts adding noise to the fidelity of on device measurements. Then, we first propose a novel lightweight measurement technique that can be used as a basis for advanced resource optimization algorithms to be run on mobile phones. Our main idea leverages an original packet dispersion based technique to estimate per user capacity. This allows passive measurements by just sampling the existing mobile traffic. Our technique is able to efficiently filter outliers introduced by mobile network schedulers and phone hardware. In order to asses and verify our measurement technique, we apply it to a diverse dataset generated by both extensive simulations and a week-long measurement campaign spanning two cities in two countries, different radio technologies, and covering all times of the day. The results demonstrate that our technique is effective even if it is provided only with a small fraction of the exchanged packets of a flow. The only requirement for the input data is that it should consist of a few consecutive packets that are gathered periodically. This makes the measurement algorithm a good candidate for inclusion in OS libraries to allow for advanced resource optimization and application-level traffic scheduling, based on current and predicted future user capacity. We proceed with another algorithm that takes advantage of the traffic generated by short-lived TCP connections, which form the majority of the mobile connections, to passively estimate the currently available bandwidth class. Our algorithm is able to extract useful information even if the TCP connection never exits the slow start phase. To the best of our knowledge, no other solution can operate with such constrained input. Our estimation method is able to achieve good precision despite artifacts introduced by the slow start behavior of TCP, mobile scheduler and phone hardware. We evaluate our solution against traces collected in 4 European countries. Furthermore, the small footprint of our algorithm allows its deployment on resource limited devices. Finally, in an attempt to face the rapid traffic increase, mobile application developers outsource their cloud infrastructure deployment and content delivery to cloud computing services and content delivery networks. Studying how these services, which we collectively denote Cloud Service Providers (CSPs), perform over Mobile Network Operators (MNOs) is crucial to understanding some of the performance limitations of today’s mobile apps. To that end, we perform the first empirical study of the complex dynamics between applications, MNOs and CSPs. First, we use real mobile app traffic traces that we gathered through a global crowdsourcing campaign to identify the most prevalent CSPs supporting today’s mobile Internet. Then, we investigate how well these services interconnect with major European MNOs at a topological level, and measure their performance over European MNO networks through a month-long measurement campaign on the MONROE mobile broadband testbed. We discover that the top 6 most prevalent CSPs are used by 85% of apps, and observe significant differences in their performance across different MNOs due to the nature of their services, peering relationships with MNOs, and deployment strategies. We also find that CSP performance in MNOs is affected by inflated path length, roaming, and presence of middleboxes, but not influenced by the choice of DNS resolver. We also observe that the choice of operator’s Point of Presence (PoP) may inflate by at least 20% the delay towards popular websites.This work has been supported by IMDEA Networks Institute.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Ahmed Elmokashfi.- Secretario: Rubén Cuevas Rumín.- Vocal: Paolo Din

Treatment-Based Classi?cation in Residential Wireless Access Points

IEEE 802.11 wireless access points (APs) act as the central communication hub inside homes, connecting all networked devices to the Internet. Home users run a variety of network applications with diverse Quality-of-Service requirements (QoS) through their APs. However, wireless APs are often the bottleneck in residential networks as broadband connection speeds keep increasing. Because of the lack of QoS support and complicated configuration procedures in most off-the-shelf APs, users can experience QoS degradation with their wireless networks, especially when multiple applications are running concurrently. This dissertation presents CATNAP, Classification And Treatment iN an AP , to provide better QoS support for various applications over residential wireless networks, especially timely delivery for real-time applications and high throughput for download-based applications. CATNAP consists of three major components: supporting functions, classifiers, and treatment modules. The supporting functions collect necessary flow level statistics and feed it into the CATNAP classifiers. Then, the CATNAP classifiers categorize flows along three-dimensions: response-based/non-response-based, interactive/non-interactive, and greedy/non-greedy. Each CATNAP traffic category can be directly mapped to one of the following treatments: push/delay, limited advertised window size/drop, and reserve bandwidth. Based on the classification results, the CATNAP treatment module automatically applies the treatment policy to provide better QoS support. CATNAP is implemented with the NS network simulator, and evaluated against DropTail and Strict Priority Queue (SPQ) under various network and traffic conditions. In most simulation cases, CATNAP provides better QoS supports than DropTail: it lowers queuing delay for multimedia applications such as VoIP, games and video, fairly treats FTP flows with various round trip times, and is even functional when misbehaving UDP traffic is present. Unlike current QoS methods, CATNAP is a plug-and-play solution, automatically classifying and treating flows without any user configuration, or any modification to end hosts or applications

Designs for the Quality of Service Support in Low-Energy Wireless Sensor Network Protocols

A Wireless Sensor Network (WSN) consists of small, low cost, and low energy sensor nodes that cooperatively monitor physical quantities, control actuators, and perform data processing tasks. A network may consist of thousands of randomly deployed self-configurable nodes that operate autonomously to form a multihop topology. This Thesis focuses on Quality of Service (QoS) in low-energy WSNs that aim at several years operation time with small batteries. As a WSN may include both critical and non-critical control and monitoring applications, QoS is needed to make intelligent, content specific trade-offs between energy and network performance. The main research problem is defining and implementing QoS with constrained energy budget, processing power, communication bandwidth, and data and program memories. The problem is approached via protocol designs and algorithms. These are verified with simulations and with measurements in practical deployments. This Thesis defines QoS for WSNs with quantifiable metrics to allow measuring and managing the network performance. The definition is used as a basis for QoS routing protocol and Medium Access Control (MAC) schemes, comprising dynamic capacity allocation algorithm and QoS support layer. Dynamic capacity allocation is targeted at reservation based MACs, whereas the QoS support layer operates on contention based MACs. Instead of optimizing the protocols for a certain use case, the protocols allow configurable QoS based on application specific requirements. Finally, this Thesis designs sensor self-diagnostics and diagnostics analysis tool for verifying network performance. Compared to the related proposals on in-network sensor diagnostics, the diagnostics also detects performance problems and identifies reasons for the issues thus allowing the correction of problems. The results show that the developed protocols allow a clear trade-off between energy, latency, throughput, and reliability aspects of QoS while incurring a minimal overhead. The feasibility of results for extremely resource constrained WSNs is verified with the practical implementation with a prototype hardware platform having only few Million Instructions Per Second (MIPS) of processing power and less than a hundred kBs data and program memories. The results of this Thesis can be used in the WSN research, development, and implementation in general. The developed QoS definition, protocols, and diagnostics tools can be used separately or adapted to other applications and protocols