Vasileios Karyotis

A novel framework for mobile attack strategy modelling and vulnerability analysis in wireless ad hoc networks

Global dissemination of information and tools for computer networks, has allowed for major system attacks affecting critical network points and resulting in significant network performance degradation. In this paper, we present a probabilistic modelling framework for the propagation of an energy-constrained mobile threat in a wireless ad hoc network. The motivation behind this approach can be found in the topology-constrained character of the ad hoc setting, its dynamic nature and the stochastic characteristics of the interactions among the involved events. The introduced formulation is used to identify and evaluate different attack strategies and approaches. Through modelling and simulation, we evaluate the impact of various parameters associated with the operational characteristics of the mobile attacker on an outbreak spreading and the network evolution. Furthermore, a new metric, which indicates the overall infection-capability of each attack strategy is proposed and used to characterise the potential of each strategy to harm the network.

Malware-Propagative Mobile Ad Hoc Networks: Asymptotic Behavior Analysis

In this paper, the spreading of malicious software over ad hoc networks, where legitimate nodes are prone to propagate the infections they receive from either an attacker or their already infected neighbors, is analyzed. Considering the Susceptible-Infected-Susceptible (SIS) node infection paradigm we propose a probabilistic model, on the basis of the theory of closed queuing networks, that aims at describing the aggregated behavior of the system when attacked by malicious nodes. Because of its nature, the model is also able to deal more effectively with the stochastic behavior of attackers and the inherent probabilistic nature of the wireless environment. The proposed model is able to describe accurately the asymptotic behavior of malware-propagative large scale ad hoc networking environments. Using the Norton equivalent of the closed queuing network, we obtain analytical results for its steady state behavior, which in turn is used for identifying the critical parameters affecting the operation of the network. Finally, through modeling and simulation, some additional numerical results are obtained with respect to the behavior of the system when multiple attackers are present, and regarding the time-dependent evolution and impact of an attack.

Exploiting socio-physical network interactions via a utility-based framework for resource management in mobile social networks

Mobile social networks have the lions share in modern mobile telecommunications, and their interaction with the underlying infrastructure networks has attracted significant attention due to its impact on resource management. In this article, we present and demonstrate a framework for addressing such interplay between online social networks and wireless communications by exploiting principles from the theory of utility-based engineering and elements from social network analysis. We aim at a holistic design framework that allows the joint development of improved resource management mechanisms for future mobile wireless infrastructures and their social counterparts. We demonstrate the proposed methodology and reveal the key aspects of designing and exploiting convenient utility functions within the framework of network science in order to better manage the available resources, improve infrastructures, and eventually obtain from them the maximum possible benefit. We establish the above principles and emerging design potentials in future complex networks by presenting two tangible examples where personalized advertising and topology control in MSNs are used to exploit and validate different network and individual socio-utility maximization features, respectively.

A hyperbolic space analytics framework for big network data and their applications

Big data analytics have generated a paradigm shift in modern data analysis and decision making in almost every aspect of human society. Nowadays, massive amounts of generated network and correlated (networked) data pose critical computational and storage challenges, requiring the development of radical techniques to manage, process, and analyze them more efficiently. We propose embedding such data and their correlations in hyperbolic metric spaces as one approach aspiring to radically change current practices. In this article, we explore the potential that such data embedding and the corresponding hyperbolic space based data analytics can offer to networks, their applications, and their services. We demonstrate how this approach may lead to more efficient and scalable problem solving within diverse application domains, such as network design/analysis, network resource allocation optimization, and network economics/marketing, paving the way for more diverse and effective solutions in the future.

Topology Enhancements in Wireless Multihop Networks: A Top-Down Approach

Contemporary traffic demands call for efficient infrastructures capable of sustaining increasing volumes of social communications. In this work, we focus on improving the properties of wireless multihop networks with social features through network evolution. Specifically, we introduce a framework, based on inverse Topology Control (iTC), for distributively modifying the transmission radius of selected nodes, according to social paradigms. Distributed iTC mechanisms are proposed for exploiting evolutionary network churn in the form of edge/node modifications, without significantly impacting available resources. We employ continuum theory for analytically describing the proposed top-down approach of infusing social features in physical topologies. Through simulations, we demonstrate how these mechanisms achieve their goal of reducing the average path length, so as to make a wireless multihop network scale like a social one, while retaining its original multihop character. We study the impact of the proposed topology modifications on the operation and performance of the network with respect to the average throughput, delay, and energy consumption of the induced network.

Evolutionary Dynamics of Complex Communications Networks

Until recently, most network design techniques employed a bottom-up approach with lower protocol layer mechanisms affecting the development of higher ones. This approach, however, has not yielded fascinating results in the case of wireless distributed networks. Addressing the emerging aspects of modern network analysis and design, Evolutionary Dynamics of Complex Communications Networks introduces and develops a top-bottom approach where elements of the higher layer can be exploited in modifying the lowest physical topologyclosing the network design loop in an evolutionary fashion similar to that observed in natural processes.This book provides a complete overview of contemporary design approaches from the viewpoint of network science and complex/social network analysis. A significant part of the text focuses on the classification and analysis of various network modification mechanisms for wireless decentralized networks that exploit social features from relevant online social networks. Each chapter begins with learning objectives and introductory material and slowly builds to more detailed analysis and advanced concepts. Each chapter also identifies open issues, while by the end of the book, potential research directions are summarized for the more interested researcher or graduate student.The approach outlined in the book will help network designers and administrators increase the value of their infrastructure without requiring any significant additional investment. Topics covered include: basic network graph models and properties, cognitive methods and evolutionary computing, complex and social network analysis metrics and features, and analysis and development of the distinctive structure and features of complex networks. Considering all aspects of modern network analysis and design, the text covers the necessary material and background to make it a suitable source of reference for graduate students, postdoctoral researchers, and scientists

Diffusion Models for Information Dissemination Dynamics in Wireless Complex Communication Networks

Information dissemination has become one of the most important services of communication networks. Modeling the diffusion of information through such networks is crucial for our modern information societies. In this work, novel models, segregating between useful and malicious types of information, are introduced, in order to better study Information Dissemination Dynamics (IDD) in wireless complex communication networks, and eventually allow taking into account special network features in IDD. According to the proposed models, and inspired from epidemiology, we investigate the IDD in various complex network types through the use of the Susceptible-Infected (SI) paradigm for useful information dissemination and the Susceptible-Infected-Susceptible (SIS) paradigm for malicious information spreading. We provide analysis and simulation results for both types of diffused information, in order to identify performance and robustness potentials for each dissemination process with respect to the characteristics of the underlying complex networking infrastructures. We demonstrate that the proposed approach can generically characterize IDD in wireless complex networks and reveal salient features of dissemination dynamics in each network type, which could eventually aid in the design of more advanced, robust, and efficient networks and services.

On the optimal, fair and channel-aware cognitive radio network reconfiguration☆

Abstract In this work, we focus on the Joint Channel Assignment and Routing (JCAR) problem in Cognitive Radio Networks (CRNs) and especially on the optimal reconfiguration of secondary networks under the presence of primary users. Secondary CRN users need to adapt their transmission channels promptly, while effectively limit additional or escalating system modifications triggered by the intertweaved primary user activity. Our approach takes into consideration the underlying spectrum switching dynamics and concurrently aims at a fair resource allocation among the active network flows. We take an optimization perspective and formulate the JCAR and network reconfiguration problems as mixed integer linear programs, addressing fairness concerns as well. We propose a heuristic approach which is based on a sequential reduced search space methodology, in order to obtain efficiently solutions of otherwise tough and demanding reconfiguration problems. The operation, effectiveness and performance of the proposed mechanisms are evaluated through analysis and simulations under various working conditions. The obtained numerical results indicate the benefits of the proposed schemes in terms of overhead performance and their scaling properties with respect to more realistic and thus demanding topologies.

A Markov Random Field framework for channel assignment in Cognitive Radio networks

In order to alleviate the absence of hierarchical coordination among secondary users of Cognitive Radio networks and to enable fast and efficient reconfiguration of pervasive ad hoc networks, spatially localized and frequency agile mechanisms are required across the wireless protocol stack. In this paper, we introduce a framework for distributed and adaptable radio channel allocation in wireless Cognitive Radio networks, which is based on the theory of Markov Random Fields and Gibbs sampling. By exchanging local only information, secondary users are able to assign efficiently available channels according to desired operational requirements, even if these requirements change over time. Through analysis and simulation we show the applicability of the framework and demonstrate its distributed operation. We study the emerging trade-offs of the proposed approach by demonstrating the performance benefits obtained in radio channel allocation and by analyzing the inherent costs.

Socially-Inspired Topology Improvements in Wireless Multi-Hop Networks

Several graph structures have emerged for describing different network types and the interactions among their entities. Various properties have been identified in each of these network graphs, with no single structure bearing all the desired features that would constitute it an optimal network graph. This work aims for the first time at infusing the desired properties of a small-world network into the core structure of a wireless multi-hop network, thus embedding social structure on an artificial network that emerges in the development of wireless communication services. In this paper, topology control based approaches are proposed, serving the purpose of adding communication links in a multi-hop network in an intelligent and effective manner. Through analysis and simulation, we demonstrate how the proposed methods decrease the average path length between randomly selected node pairs and properly scale the clustering coefficient of a multi-hop network, by exploiting social structure characteristics of small-world networks, thus allowing the development of more demanding services on top of wireless multi- hop networks.