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From cyber-security deception to manipulation and gratification through gamification

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arXiv 2019年

作者： Bellekens, Xavier Jayasekara, Gayan Hindy, Hanan Bures, Miroslav Brosset, David Tachtatzis, Christos Atkinson, Robert Abertay University Division of Cyber-Security United Kingdom Naval Cyber Defense Ecole navale France Czech Technical University in Prague Dpt. of Computer Science Faculty of Electrical Engineering Czech Republic University of Strathclyde Dpt. of Electronic & Electrical Engineering United Kingdom

With the ever growing networking capabilities and services offered to users, attack surfaces have been increasing exponentially, additionally, the intricacy of network architectures has increased the complexity of cyber-defenses, to this end, the use of deception has recently been trending both in academia and industry. Deception enables to create proactive defense systems, luring attackers in order to better defend the systems at hand. Current applications of deception, only rely on static, or low interactive environments. In this paper we present a platform that combines human-computer-interaction, analytics, gamification and deception to lure malicious users into selected traps while piquing their interests. Furthermore we analyse the interactive deceptive aspects of the platform through the addition of a narrative, further engaging malicious users into following a predefined path and deflecting attacks from key network systems. Copyright © 2019, The Authors. All rights reserved.

关键词： Cybersecurity

Active flow control for external aerodynamics: from micro air vehicles to a full aircraft in stall

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Journal of Physics: Conference Series 2020年第1期1522卷

作者： Oriol Lehmkuhl Adrián Lozano-Durán Ivette Rodriguez Dpt. of Computer Applications in Science and Engineering Barcelona Supercomputing Center Spain Center for Turbulence Research Stanford University USA Dpt. of Heat Engines Universitat Politecnica de Catalunya Spain

We investigate the aerodynamic performance of active flow control of airfoils and wings using synthetic jets with zero net-mass flow. The study is conducted via wall-resolved and wall-modeled large-eddy simulation using two independent CFD solvers: Alya, a finite-element-based solver; and charLES, a finite-volume-based solver. Our approach is first validated in a NACA4412, for which numerical and experimental results are already available in the literature. The performance of synthetic jets is evaluated for two flow configurations: a SD7003 airfoil at moderate Reynolds number with laminar separation bubble, which is representative of Micro Air Vehicles, and the high-lift configuration of the JAXA Standard Model at realistic Reynolds numbers for landing. In both cases, our predictions indicate that, at high angles of attack, the control successfully eliminates the laminar/turbulent recirculations located downstream the actuator, which increases the aerodynamic performance. Our efforts illustrate the technology-readiness of large-eddy simulation in the design of control strategies for real-world external aerodynamic applications.

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Estimating the Jones polynomial for Ising anyons on noisy quantum computers

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arXiv 2022年

作者： Self, Chris N. Iblisdir, Sofyan Brennen, Gavin K. Meichanetzidis, Konstantinos QOLS Blackett Laboratory Imperial College London SW7 2AZ United Kingdom Quantinuum Partnership House Carlisle Place LondonSW1P 1BX United Kingdom Dpto. Análisis Matemático y Matemática Aplicada Facultad de Matemáticas Universidad Complutense Madrid28040 Spain Dpt. Astrofísica i Física Quàntica Institut de Ciències del Cosmos Facultat de Física Universitat de Barcelona Barcelona08028 Spain Center for Engineered Quantum Systems Department of Physics & Astronomy Macquarie University NSW2109 Australia Department of Computer Science University of Oxford United Kingdom Quantinuum 17 Beaumont St. OxfordOX1 2NA United Kingdom

The evaluation of the Jones polynomial at roots of unity is a paradigmatic problem for quantum computers. In this work we present experimental results obtained from existing noisy quantum computers for special cases of this problem, where it is classically tractable. Our approach relies on the reduction of the problem of evaluating the Jones polynomial of a knot at lattice roots of unity to the problem of computing quantum amplitudes of qudit stabiliser circuits, which are classically efficiently simulatable. More specifically, we focus on evaluation at the fourth root of unity, which is a lattice root of unity, where the problem reduces to evaluating amplitudes of qubit stabiliser circuits. To estimate the real and imaginary parts of the amplitudes up to additive error we use the Hadamard test. We further argue that this setup defines a standard benchmark for near-term noisy quantum processors. Furthermore, we study the benefit of performing quantum error mitigation with the method of zero noise extrapolation. Copyright © 2022, The Authors. All rights reserved.

关键词： Polynomials

Non-Parametric Bootstrap Detection of Availability Service Level Objective Violations in Cloud Storage

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Non-Parametric Bootstrap Detection of Availability Service L...

Network Traffic Measurement and Analysis Conference (TMA)

作者： Maurizio Naldi Dpt. of Civil Engineering and Computer Science University of Rome Tor Vergata Rome Italy

Service quality commitments in cloud service provisioning are typically described in Service Level Agreements (SLA). Service availability is always a major parameter to be included in such SLAs. and the cloud provider is bounded to guarantee a minimum availability value, for which current cloud monitoring systems employ a naive estimator. In this paper a new estimation method is proposed for service availability, which is based on the bootstrap technique and employs a non-parametric statistical hypothesis test. Through Monte Carlo simulation, the method is shown to be much more accurate than the naive one under three stochastic models for the durations of operating and outage periods, exhibiting a Type I error probability lower than 1 % in most cases, while the naive estimator yields error probabilities around 40%.

关键词： Cloud computing Mathematical model Computational modeling Error probability Monitoring Estimation Monte Carlo methods

A mixture of experts model for predicting persistent weather patterns

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arXiv 2019年

作者： Pérez-Ortiz, M. Gutiérrez, P.A. Tino, P. Casanova-Mateo, C. Salcedo-Sanz, S. Computer Laboratory University of Cambridge United Kingdom Dpt. of Computer Science and Numerical Analysis University of Córdoba Spain School of Computer Science University of Birmingham United Kingdom Department of Civil Engineering: Construction Infrastructure and Transport Universidad Politécnica de Madrid Madrid Spain Department of Signal Processing and Communications University of Alcalá Spain

Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system will be similar (or equal) to the present state. Machine learning (ML) models are widely used in different weather forecasting applications, but they need to be compared to the persistence model to analyse whether they provide a competitive solution to the problem at hand. In this paper, we devise a new model for predicting low-visibility in airports using the concepts of mixture of experts. Visibility level is coded as two different ordered categorical variables: cloud height and runway visual height. The underlying system in this application is stagnant approximately in 90% of the cases, and standard ML models fail to improve on the performance of the persistence model. Because of this, instead of trying to simply beat the persistence model using ML, we use this persistence as a baseline and learn an ordinal neural network model that refines its results by focusing on learning weather fluctuations. The results show that the proposal outperforms persistence and other ordinal autoregressive models, especially for longer time horizon predictions and for the runway visual height variable. Copyright © 2019, The Authors. All rights reserved.

关键词： Weather forecasting

PROBABILISTIC MATRIX COMPLETION FOR IMAGE PHASE RETRIEVAL

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PROBABILISTIC MATRIX COMPLETION FOR IMAGE PHASE RETRIEVAL

IEEE International Conference on Image Processing

作者： Petros Nyfantis Pablo Ruiz Aggelos K. Katsaggelos Department of Physics National and Kapodistrian University of Athens Dpt. of Electrical Engineering and Computer Science Northwestern University

In this paper we address the Phase Retrieval problem, which aims to recover the phase of the Fourier transform of a signal when only magnitude measurements are available. Following recent developments in Phase Retrieval, the problem can be transformed into a convex semidefinite programming optimization problem, which can be solved using Matrix Completion techniques. In this paper the acquisition process is modeled using a likelihood function, which splits the original problem into two convex optimization problems, and alternates between the solution of each of them. To relate both convex problems we introduce a heuristic, which results in fast convergence of the proposed method.

关键词： Extraterrestrial measurements Optimization Convergence Minimization Heuristic algorithms Discrete Fourier transforms

Improving skip-gram based graph embeddings via centrality-weighted sampling

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arXiv 2019年

作者： Blanco, Pedro Almagro Caparrini, Fernando Sancho Departament de Física de la Matèria Condensada Universitat de Barcelona Martí Franquès 1 BarcelonaE-08028 Spain Complex Systems Modelling Group Universidad Central de Ecuador Quito170129 Ecuador Dpt. Computer Science and Artificial Intelligence University of Sevilla Sevilla41012 Spain

Network embedding techniques inspired by word2vec represent an effective unsupervised relational learning model. Commonly, by means of a Skip-Gram procedure, these techniques learn low dimensional vector representations of the nodes in a graph by sampling node-context examples. Although many ways of sampling the context of a node have been proposed, the effects of the way a node is chosen have not been analyzed in depth. To fill this gap, we have re-implemented the main four word2vec inspired graph embedding techniques under the same framework and analyzed how different sampling distributions affects embeddings performance when tested in node classification problems. We present a set of experiments on different well known real data sets that show how the use of popular centrality distributions in sampling leads to improvements, obtaining speeds of up to 2 times in learning times and increasing accuracy in all cases. Copyright © 2019, The Authors. All rights reserved.

关键词： Graph embeddings

EEG time series learning and classification using a hybrid forecasting model calibrated with GVNS

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Electronic Notes in Discrete Mathematics 2017年 58卷 79-86页

作者： Coelho, V.N. Coelho, I.M. Coelho, B.N. Souza, M.J.F. Guimarães, F.G. Luz, E.J. da S. Barbosa, A.C. Coelho, M.N. Netto, G.G. Costa, R.C. Pinto, A.A. Figueiredo, A. de P. Elias, M.E.V. Filho, D.C.O.G. Oliveira, T.A. Grupo da Causa Humana Ouro Preto Brazil Instituto de Pesquisa e Desenvolvimento de Tecnologias Ouro Preto Brazil Dpt. of Computer Science at Universidade Estadual do Rio de Janeiro Brazil Dpt. of Computer Science at Universidade Federal de Ouro Preto Brazil Dpt. of Electrical Engineering at Universidade Federal de Minas Gerais Brazil

Brain activity can be seen as a time series, in particular, electroencephalogram (EEG) can measure it over a specific time period. In this regard, brain fingerprinting can be subjected to be learned by machine learning techniques. These models have been advocated as EEG-based biometric systems. In this study, we apply a recent Hybrid Focasting Model, which calibrates its if-then fuzzy rules with a hybrid GVNS metaheuristic algorithm, in order to learn those patterns. Due to the stochasticity of the VNS procedure, models with different characteristics can be generated for each individual. Some EEG recordings from 109 volunteers, measured using a 64-channels EEGs, with 160 HZ of sampling rate, are used as cases of study. Different forecasting models are calibrated with the GVNS and used for the classification purpose. New rules for classifying the individuals using forecasting models are introduced. Computational results indicate that the proposed strategy can be improved and embedded in the future biometric systems. © 2017 Elsevier B.V.

关键词： Biometrics Brain fingerprinting Electroencephalogram Forecasting and Time series Variable Neighborhood Search

Modiàed Kirchhoff diffraction of pulsed EM waves radiated from a slot-excited Fabry-Pérot resonator antenna 12

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Modiàed Kirchhoff diffraction of pulsed EM waves radiated f...

12th European Conference on Antennas and Propagation, EuCAP 2018

作者： Stumpf, Martin Vandenbosch, Guy A.E. Lager, Ioan E. SIX Research Centre Brno University of Technology Technická 3082/12 Brno616 00 Czech Republic Electrical Engineering Dpt. TELEMIC Katholieke Universiteit Leuven Kasteelpark Arenberg 10 bus 2444 LeuvenB-3001 Belgium Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands

ISBN: (纸本)9781785617911

Diffraction of pulsed electromagnetic (EM) waves in a slot excited Fabry Perót type resonator antenna is studied analytically in the modiÀed Kirchhoff approximation. Closed-form space-time expressions for the radiated EM Àelds are found using the Cagniard-DeHoop technique. Illustrative numerical examples are given. © 2018 Institution of engineering and *** Rights Reserved.

关键词： Diffraction