17,379 research outputs found

    Stochastic Tools for Network Intrusion Detection

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    With the rapid development of Internet and the sharp increase of network crime, network security has become very important and received a lot of attention. We model security issues as stochastic systems. This allows us to find weaknesses in existing security systems and propose new solutions. Exploring the vulnerabilities of existing security tools can prevent cyber-attacks from taking advantages of the system weaknesses. We propose a hybrid network security scheme including intrusion detection systems (IDSs) and honeypots scattered throughout the network. This combines the advantages of two security technologies. A honeypot is an activity-based network security system, which could be the logical supplement of the passive detection policies used by IDSs. This integration forces us to balance security performance versus cost by scheduling device activities for the proposed system. By formulating the scheduling problem as a decentralized partially observable Markov decision process (DEC-POMDP), decisions are made in a distributed manner at each device without requiring centralized control. The partially observable Markov decision process (POMDP) is a useful choice for controlling stochastic systems. As a combination of two Markov models, POMDPs combine the strength of hidden Markov Model (HMM) (capturing dynamics that depend on unobserved states) and that of Markov decision process (MDP) (taking the decision aspect into account). Decision making under uncertainty is used in many parts of business and science.We use here for security tools.We adopt a high-quality approximation solution for finite-space POMDPs with the average cost criterion, and their extension to DEC-POMDPs. We show how this tool could be used to design a network security framework.Comment: Accepted by International Symposium on Sensor Networks, Systems and Security (2017

    Statistical physics of isotropic-genesis nematic elastomers: I. Structure and correlations at high temperatures

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    Isotropic-genesis nematic elastomers (IGNEs) are liquid crystalline polymers (LCPs) that have been randomly, permanently cross-linked in the high-temperature state so as to form an equilibrium random solid. Thus, instead of being free to diffuse throughout the entire volume, as they would be in the liquid state, the constituent LCPs in an IGNE are mobile only over a finite length-scale controlled by the density of cross-links. We address the effects that such network-induced localization have on the liquid-crystalline characteristics of an IGNE, as probed via measurements made at high temperatures. In contrast with the case of uncross-linked LCPs, for IGNEs these characteristics are determined not only by thermal fluctuations but also by the quenched disorder associated with the cross-link constraints. To study IGNEs, we consider a microscopic model of dimer nematogens in which the dimers interact via orientation-dependent excluded volume forces. The dimers are, furthermore, randomly, permanently cross-linked via short Hookean springs, the statistics of which we model by means of a Deam-Edwards type of distribution. We show that at length-scales larger than the size of the nematogens this approach leads to a recently proposed phenomenological Landau theory of IGNEs [Lu et al., Phys. Rev. Lett. 108, 257803 (2012)], and hence predicts a regime of short-ranged oscillatory spatial correlations in the nematic alignment, of both thermal and glassy types. In addition, we consider two alternative microscopic models of IGNEs: (i) a wormlike chain model of IGNEs that are formed via the cross-linking of side-chain LCPs; and (ii) a jointed chain model of IGNEs that are formed via the cross-linking of main-chain LCPs. At large length-scales, both of these models give rise to liquid-crystalline characteristics that are qualitatively in line with those predicted by the dimer-and-springs model.Comment: 33 pages, 6 figures, 6 appendice

    Making an impact: The influence of policies to reduce emissions from aviation on the business travel patterns of individual corporations

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    The contribution of aviation to global carbon dioxide (CO2) emissions is projected to triple by 2050. As nations strive to meet CO2 reduction targets, policy interventions to manage the growth of emissions arising from air travel are likely. Here, we investigate the potential influence of aviation emissions reduction policies on the business travel patterns of individual corporations. Using travel data from six UK-based companies, we find that increased ticket prices can deliver substantial emissions cuts, particularly on premium class flights, and may provide strong financial incentives to seek modal and/or technological alternatives to flying. We also find that corporations from different business sectors vary in their responsiveness to arange of policy options. Finally, we examine questionnaire data to determine whether companies more broadly are going beyond compliance to mitigate their environmental impact by managing travel-related emissions voluntarily. Although many corporations are measuring and reporting emissions, only a limited number are willing to implement in-house reduction policies prior to regulation
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