7,976 research outputs found

    A Diversity-based Substation Cyber Defense Strategy utilizing Coloring Games

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    Growing cybersecurity risks in the power grid require that utilities implement a variety of security mechanism (SM) composed mostly of VPNs, firewalls, or other custom security components. While they provide some protection, they might contain software vulnerabilities which can lead to a cyber-attack. In this paper, the severity of a cyber-attack has been decreased by employing a diverse set of SM that reduce repetition of a single vulnerability. This paper focuses on the allocation of diverse SM and tries to increase the security of the cyber assets located within the electronic security perimeter(ESP) of a substation. We have used a graph-based coloring game in a distributed manner to allocate diverse SM for protecting the cyber assets. The vulnerability assessment for power grid network is also analyzed using this game theoretic method. An improved, diversified SMs for worst-case scenario has been demonstrated by reaching the Nash equilibrium of graph coloring game. As a case study, we analyze the IEEE-14 and IEEE-118 bus system, observe the different distributed coloring algorithm for allocating diverse SM and calculating the overall network criticality.Comment: 8 pages, 6 tables and 8 figure

    The topology of covert conflict

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    Often an attacker tries to disconnect a network by destroying nodes or edges, while the defender counters using various resilience mechanisms. Examples include a music industry body attempting to close down a peer-to-peer file-sharing network; medics attempting to halt the spread of an infectious disease by selective vaccination; and a police agency trying to decapitate a terrorist organisation. Albert, Jeong and Barabasi famously analysed the static case, and showed that vertex-order attacks are effective against scale-free networks. We extend this work to the dynamic case by developing a framework based on evolutionary game theory to explore the interaction of attack and defence strategies. We show, first, that naive defences don't work against vertex-order attack; second, that defences based on simple redundancy don't work much better, but that defences based on cliques work well; third, that attacks based on centrality work better against clique defences than vertex-order attacks do; and fourth, that defences based on complex strategies such as delegation plus clique resist centrality attacks better than simple clique defences. Our models thus build a bridge between network analysis and evolutionary game theory, and provide a framework for analysing defence and attack in networks where topology matters. They suggest definitions of efficiency of attack and defence, and may even explain the evolution of insurgent organisations from networks of cells to a more virtual leadership that facilitates operations rather than directing them. Finally, we draw some conclusions and present possible directions for future research.Comment: University of Cambridge Technical Repor

    A Game-Theoretic Taxonomy and Survey of Defensive Deception for Cybersecurity and Privacy

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    Cyberattacks on both databases and critical infrastructure have threatened public and private sectors. Ubiquitous tracking and wearable computing have infringed upon privacy. Advocates and engineers have recently proposed using defensive deception as a means to leverage the information asymmetry typically enjoyed by attackers as a tool for defenders. The term deception, however, has been employed broadly and with a variety of meanings. In this paper, we survey 24 articles from 2008-2018 that use game theory to model defensive deception for cybersecurity and privacy. Then we propose a taxonomy that defines six types of deception: perturbation, moving target defense, obfuscation, mixing, honey-x, and attacker engagement. These types are delineated by their information structures, agents, actions, and duration: precisely concepts captured by game theory. Our aims are to rigorously define types of defensive deception, to capture a snapshot of the state of the literature, to provide a menu of models which can be used for applied research, and to identify promising areas for future work. Our taxonomy provides a systematic foundation for understanding different types of defensive deception commonly encountered in cybersecurity and privacy.Comment: To Appear in ACM Cumputing Surveys (CSUR

    On Game-Theoretic Risk Management (Part Three) - Modeling and Applications

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    The game-theoretic risk management framework put forth in the precursor reports "Towards a Theory of Games with Payoffs that are Probability-Distributions" (arXiv:1506.07368 [q-fin.EC]) and "Algorithms to Compute Nash-Equilibria in Games with Distributions as Payoffs" (arXiv:1511.08591v1 [q-fin.EC]) is herein concluded by discussing how to integrate the previously developed theory into risk management processes. To this end, we discuss how loss models (primarily but not exclusively non-parametric) can be constructed from data. Furthermore, hints are given on how a meaningful game theoretic model can be set up, and how it can be used in various stages of the ISO 27000 risk management process. Examples related to advanced persistent threats and social engineering are given. We conclude by a discussion on the meaning and practical use of (mixed) Nash equilibria equilibria for risk management

    Facing Multiple Attacks in Adversarial Patrolling Games with Alarmed Targets

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    We focus on adversarial patrolling games on arbitrary graphs, where the Defender can control a mobile resource, the targets are alarmed by an alarm system, and the Attacker can observe the actions of the mobile resource of the Defender and perform different attacks exploiting multiple resources. This scenario can be modeled as a zero-sum extensive-form game in which each player can play multiple times. The game tree is exponentially large both in the size of the graph and in the number of attacking resources. We show that when the number of the Attacker's resources is free, the problem of computing the equilibrium path is NP-hard, while when the number of resources is fixed, the equilibrium path can be computed in poly-time. We provide a dynamic-programming algorithm that, given the number of the Attacker's resources, computes the equilibrium path requiring poly-time in the size of the graph and exponential time in the number of the resources. Furthermore, since in real-world scenarios it is implausible that the Defender knows the number of attacking resources, we study the robustness of the Defender's strategy when she makes a wrong guess about that number. We show that even the error of just a single resource can lead to an arbitrary inefficiency, when the inefficiency is defined as the ratio of the Defender's utilities obtained with a wrong guess and a correct guess. However, a more suitable definition of inefficiency is given by the difference of the Defender's utilities: this way, we observe that the higher the error in the estimation, the higher the loss for the Defender. Then, we investigate the performance of online algorithms when no information about the Attacker's resources is available. Finally, we resort to randomized online algorithms showing that we can obtain a competitive factor that is twice better than the one that can be achieved by any deterministic online algorithm

    Optimal Secure Multi-Layer IoT Network Design

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    With the remarkable growth of the Internet and communication technologies over the past few decades, Internet of Things (IoTs) is enabling the ubiquitous connectivity of heterogeneous physical devices with software, sensors, and actuators. IoT networks are naturally two-layer with the cloud and cellular networks coexisting with the underlaid device-to-device (D2D) communications. The connectivity of IoTs plays an important role in information dissemination for mission-critical and civilian applications. However, IoT communication networks are vulnerable to cyber attacks including the denial-of-service (DoS) and jamming attacks, resulting in link removals in IoT network. In this work, we develop a heterogeneous IoT network design framework in which a network designer can add links to provide additional communication paths between two nodes or secure links against attacks by investing resources. By anticipating the strategic cyber attacks, we characterize the optimal design of secure IoT network by first providing a lower bound on the number of links a secure network requires for a given budget of protected links, and then developing a method to construct networks that satisfy the heterogeneous network design specifications. Therefore, each layer of the designed heterogeneous IoT network is resistant to a predefined level of malicious attacks with minimum resources. Finally, we provide case studies on the Internet of Battlefield Things (IoBT) to corroborate and illustrate our obtained results.Comment: 12 pages, to appear in IEEE Transactions on Control of Network System

    Physically-interpretable classification of biological network dynamics for complex collective motions

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    Understanding biological network dynamics is a fundamental issue in various scientific and engineering fields. Network theory is capable of revealing the relationship between elements and their propagation; however, for complex collective motions, the network properties often transiently and complexly change. A fundamental question addressed here pertains to the classification of collective motion network based on physically-interpretable dynamical properties. Here we apply a data-driven spectral analysis called graph dynamic mode decomposition, which obtains the dynamical properties for collective motion classification. Using a ballgame as an example, we classified the strategic collective motions in different global behaviours and discovered that, in addition to the physical properties, the contextual node information was critical for classification. Furthermore, we discovered the label-specific stronger spectra in the relationship among the nearest agents, providing physical and semantic interpretations. Our approach contributes to the understanding of principles of biological complex network dynamics from the perspective of nonlinear dynamical systems.Comment: 42 pages with 7 figures and 3 tables. The latest version is published in Scientific Reports, 202

    Time Critical Social Mobilization: The DARPA Network Challenge Winning Strategy

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    It is now commonplace to see the Web as a platform that can harness the collective abilities of large numbers of people to accomplish tasks with unprecedented speed, accuracy and scale. To push this idea to its limit, DARPA launched its Network Challenge, which aimed to "explore the roles the Internet and social networking play in the timely communication, wide-area team-building, and urgent mobilization required to solve broad-scope, time-critical problems." The challenge required teams to provide coordinates of ten red weather balloons placed at different locations in the continental United States. This large-scale mobilization required the ability to spread information about the tasks widely and quickly, and to incentivize individuals to act. We report on the winning team's strategy, which utilized a novel recursive incentive mechanism to find all balloons in under nine hours. We analyze the theoretical properties of the mechanism, and present data about its performance in the challenge.Comment: 25 pages, 6 figure

    Periodic Patrols on the Line and Other Networks

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    We consider a patrolling game on a graph recently introduced by Alpern et al. (2011) where the Patroller wins if he is at the attacked node while the attack is taking place. This paper studies the periodic patrolling game in the case that the attack duration is two periods. We show that if the Patroller's period is even, the game can be solved on any graph by finding the fractional covering number and fractional independence number of the graph. We also give a complete solution to the periodic patrolling game on line graphs of arbitrary size, extending the work of Papadaki et al. (2016) to the periodic domain. This models the patrolling problem on a border or channel, which is related to a classical problem of operational research going back to Morse and Kimball (1951). A periodic patrol is required to start and end at the same location, for example the place where the Patroller leaves his car to begin a foot patrol

    BASCPS: How does behavioral decision making impact the security of cyber-physical systems?

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    We study the security of large-scale cyber-physical systems (CPS) consisting of multiple interdependent subsystems, each managed by a different defender. Defenders invest their security budgets with the goal of thwarting the spread of cyber attacks to their critical assets. We model the security investment decisions made by the defenders as a security game. While prior work has used security games to analyze such scenarios, we propose behavioral security games, in which defenders exhibit characteristics of human decision making that have been identified in behavioral economics as representing typical human cognitive biases. This is important as many of the critical security decisions in our target class of systems are made by humans. We provide empirical evidence for our behavioral model through a controlled subject experiment. We then show that behavioral decision making leads to a suboptimal pattern of resource allocation compared to non-behavioral decision making. We illustrate the effects of behavioral decision making using two representative real-world interdependent CPS. In particular, we identify the effects of the defenders' security budget availability and distribution, the degree of interdependency among defenders, and collaborative defense strategies, on the degree of suboptimality of security outcomes due to behavioral decision making. In this context, the adverse effects of behavioral decision making are most severe with moderate defense budgets. Moreover, the impact of behavioral suboptimal decision making is magnified as the degree of the interdependency between subnetworks belonging to different defenders increases. We also observe that selfish defense decisions together with behavioral decisions significantly increase security risk.Comment: 32 page
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