Jamming Attack Resiliency and Performance Analysis of Cognitive Radio Communication Networks

Cognitive radio technology emerges as a promising solution for overcoming shortage and inefficient use of spectrum resources. In cognitive radio networks, secondary users, which are users equipped with cognitive radios, can opportunistically access spectrum assigned to primary users, the spectrum license holders. Although it improves spectrum utilization efficiency, this opportunistic spectrum access incurs undesired delays that can degrade the quality of service (QoS) of delay-sensitive applications substantially. It is therefore important to understand, model, and characterize these delays, as well as their dependency on primary user behaviors. Moreover, the lack of access priority leads to significant performance degradation when the network is under jamming attacks. It turns out that addressing jamming attacks while maintaining a desired QoS is very challenging. In this thesis, we characterize the properties of the random process that describes the availability of the opportunistic resources, and analytically model and analyze cognitive network average delays. Furthermore, we propose and study new techniques that mitigate jamming attacks in mobile cognitive radio networks. More specifically, this thesis consists of the following three complimentary frameworks:Bechir Hamdaoui1. Stochastic Resource Availability Modeling and Delay Analysis. In this framework, we define and characterize the properties of the random process that describes the availability of the opportunistic network resources. We apply the mean residual service time concept to derive an analytical solution for the cognitive network queueing delay. We model the service mechanism, and determine the manner in which it depends on spectrum availability. We show that the delay becomes unbounded if spectrum dynamics are not carefully considered in network design.2. Mitigating Jamming through Pseudorandom Time Hopping. In this framework, we propose and evaluate jamming countermeasure approaches for mobile cognitive users. We propose two time-based techniques which, unlike other existing frequency-based techniques, do not assume accessibility to multiple channels and hence do not rely on spectrum handoff to countermeasure jamming. In these two techniques, we allocate data over time based on cryptographic and estimation methods. We derive analytical expressions of the jamming, switching and error probabilities. Our findings show that our proposed technique outperforms other existing frequency-based techniques.3. Optimally Controlled Time-Hopping Anti-Jamming Technique. In this framework, we propose a jamming and environment aware resource allocation method for mobile cognitive users. We propose to mitigate jamming based on an optimal allocation of data over time. In addition, we optimally control network mobility to meet a desired QoS. Our findings show that our proposed technique achieves better QoS than those achieved by existing cryptographic methods while not compromising jamming resiliency.Keywords: Performance Analysis, Delay Analysis, Cognitive Radio Networks, Jamming Attack Resilienc

A Resource Sharing Method for Reliable Slice as a Service Provisioning in 5G Metro Networks

This paper proposes a dynamic slice provisioning analysis in a 5G metro network with reliability guarantees and possible sharing of backup resources. Performance of dedicated (DP) and shared (SP) protection solutions are evaluated with respect to slice resource allocation (i.e., bandwidth and processing units). The main results show a remarkable saving, in terms of slice acceptance rate, by applying SP solutions with respect to conventional DP ones

Cloudlet computing : recent advances, taxonomy, and challenges

A cloudlet is an emerging computing paradigm that is designed to meet the requirements and expectations of the Internet of things (IoT) and tackle the conventional limitations of a cloud (e.g., high latency). The idea is to bring computing resources (i.e., storage and processing) to the edge of a network. This article presents a taxonomy of cloudlet applications, outlines cloudlet utilities, and describes recent advances, challenges, and future research directions. Based on the literature, a unique taxonomy of cloudlet applications is designed. Moreover, a cloudlet computation offloading application for augmenting resource-constrained IoT devices, handling compute-intensive tasks, and minimizing the energy consumption of related devices is explored. This study also highlights the viability of cloudlets to support smart systems and applications, such as augmented reality, virtual reality, and applications that require high-quality service. Finally, the role of cloudlets in emergency situations, hostile conditions, and in the technological integration of future applications and services is elaborated in detail. © 2013 IEEE

Resource Allocation for Interference Management in Wireless Networks

Interference in wireless networks is a major problem that impacts system performance quite substantially. Combined with the fact that the spectrum is limited and scarce, the performance and reliability of wireless systems significantly deteriorates and, hence, communication sessions are put at the risk of failure. In an attempt to make transmissions resilient to interference and, accordingly, design robust wireless systems, a diverse set of interference mitigation techniques are investigated in this dissertation. Depending on the rationale motivating the interfering node, interference can be divided into two categories, communication and jamming. For communication interference such as the interference created by legacy users(e.g., primary user transmitters in a cognitive radio network) at non-legacy or unlicensed users(e.g.,secondary user receivers), two mitigation techniques are presented in this dissertation. One exploits permutation trellis codes combined with M-ary frequency shift keying in order to make SU transmissions resilient to PUs’ interference, while the other utilizes frequency allocation as a mitigation technique against SU interference using Matching theory. For jamming interference, two mitigation techniques are also investigated here. One technique exploits time and structures a jammer mitigation framework through an automatic repeat request protocol. The other one utilizes power and, following a game-theoretic framework, employs a defense strategy against jamming based on a strategic power allocation. Superior performance of all of the proposed mitigation techniques is shown via numerical results

A Channel Selection Model based on Trust Metrics for Wireless Communications

Dynamic allocation of frequency resources to nodes in a wireless communication network is a well-known method adopted to mitigate potential interference, both unintentional and malicious. Various selection approaches have been adopted in literature, to limit the impact of interference and keep a high quality of wireless links. In this paper, we propose a different channel selection method, based on trust policies. The trust management approach proposed in this work relies on the node’s own experience and trust recommendations provided by its neighbourhood. By means of simulation results in Network Simulator NS-3, we demonstrate the effectiveness of the proposed trust method, while the system is under jamming attacks, in respect of a baseline approach. We also consider and evaluate the resilience of our approach in respect of malicious nodes, providing false information regarding the quality of the channel, to induct bad channel selection of the node. Results show how the system is resilient in respect of malicious nodes, keeping around 10% of throughput more than an approach only based on the own proper experience, considering the presence of 40% of malicious nodes, both single and collusive attacks

Network Slicing Automation: Challenges and Benefits

Network slicing is a technique widely used in 5G networks where multiple logical networks (i.e., slices) run over a single shared physical infrastructure. Each slice may realize one or multiple services, whose specific requirements are negotiated beforehand and regulated through Service Level Agreements (SLAs).\ua0 In Beyond 5G (B5G) networks it is envisioned that slices should be created, deployed, and managed in an automated fashion (i.e., without human intervention) irrespective of the technological and administrative domains over which a slice may span.\ua0Achieving this vision requires a combination of novel physical layer technologies, artificial intelligence tools, standard interfaces, network function virtualization, and software-defined networking principles. This paper provides an overview of the challenges facing network slicing automation with a focus on transport networks. Results from a selected group of use cases show the benefits of applying conventional optimization tools and machine-learning-based techniques while addressing some slicing design and provisioning problems

Machine Learning Threatens 5G Security

Machine learning (ML) is expected to solve many challenges in the fifth generation (5G) of mobile networks. However, ML will also open the network to several serious cybersecurity vulnerabilities. Most of the learning in ML happens through data gathered from the environment. Un-scrutinized data will have serious consequences on machines absorbing the data to produce actionable intelligence for the network. Scrutinizing the data, on the other hand, opens privacy challenges. Unfortunately, most of the ML systems are borrowed from other disciplines that provide excellent results in small closed environments. The resulting deployment of such ML systems in 5G can inadvertently open the network to serious security challenges such as unfair use of resources, denial of service, as well as leakage of private and confidential information. Therefore, in this article we dig into the weaknesses of the most prominent ML systems that are currently vigorously researched for deployment in 5G. We further classify and survey solutions for avoiding such pitfalls of ML in 5G systems

A Survey on Modality Characteristics, Performance Evaluation Metrics, and Security for Traditional and Wearable Biometric Systems

Biometric research is directed increasingly towards Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security and privacy. To bridge the gap, this paper accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges and future directions of research in these areas. In doing so, the paper provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design