1,733 research outputs found

    The forest through the trees:Making sense of an ecological dynamics approach to measuring and developing collective behaviour in football

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    In this book, we interpret the literature that has analysed football performance from a tactical standpoint using an ecological dynamics perspective. This approach focuses on the performer–environment relationship and provides a basis for understanding the dynamic nature of performance in collective team sports (1) and will be explained in detail throughout. The first section of this text will provide a brief description of association football as well as commonly used methods to analyse football performance. The next section will briefly introduce common theories and practices used to measure team behaviour, decision-making, and performance enhancement in team sport, which are then used to introduce the ecological dynamics framework. This framework will then be used to aid the application of these findings for tactical analysis in team sports such as football. Finally, we will introduce some of the scientific literature on improving team performance, particularly in reference to team coordination and decision-making. The following sections of this book will deal specifically with how small-sided games can be used to develop tactical behaviour in football. A small-sided games approach was chosen as these modified games allow for the simultaneous development of players’ technical skills, conditioning, and ability to solve and overcome tactical challenges through coordinative behaviour and effective decision-making (2-5). Small-sided games provide an environment that mimics the perception–action couplings of in situ performance, which should, in theory, improve the transferability of learned behaviours to in-game performance (4, 6). As a result, small-sided games are often used by coaches and form an integral part of this text. Finally, we conclude with some recommendations for future research, and some practical considerations for coaches interested in applying the research discussed in this book

    Adaptive conflict-free optimization of rule sets for network security packet filtering devices

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    Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices

    Missile Defence and Interceptor Allocation by LVQ-RBFMulti-agent Hybrid Architecture

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    This paper proposes a solution methodology for a missile defence problem using theatremissile defence (TMD) concept. In the missile defence scenario, the concept of TMD is generallyused for the optimal allocation of interceptors to counter the attack missiles. The problem iscomputationally complex due to the presence of enormous state space. The Learning vectorquantiser–Radial basis function (LVQ-RBF) multi-agent hybrid neural architecture is used as thelearning structure, and Q-learning as the learning method. The LVQ-RBF multi-agent hybridneural architecture overcomes the complex state space issue using the partitioning and weightedlearning approach. The proposed LVQ-RBF multi- agent hybrid architecture improvises thelearning performance by the local and global error criterion. The state space is explored withinitial coarse partitioning by LVQ neural network. The fine partitioning of the state space isperformed using the multi-agent RBF neural network. The discrete reward scheme is used forLVQ-RBF multi-agent hybrid neural architecture. It has a hierarchical architecture which enablesquicker convergence without the loss of accuracy. The simulation of the TMD is performed with500 assets and six priority of assets

    Security Evaluation of Support Vector Machines in Adversarial Environments

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    Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering. However, if SVMs are to be incorporated in real-world security systems, they must be able to cope with attack patterns that can either mislead the learning algorithm (poisoning), evade detection (evasion), or gain information about their internal parameters (privacy breaches). The main contributions of this chapter are twofold. First, we introduce a formal general framework for the empirical evaluation of the security of machine-learning systems. Second, according to our framework, we demonstrate the feasibility of evasion, poisoning and privacy attacks against SVMs in real-world security problems. For each attack technique, we evaluate its impact and discuss whether (and how) it can be countered through an adversary-aware design of SVMs. Our experiments are easily reproducible thanks to open-source code that we have made available, together with all the employed datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector Machine Applications

    Modelling Telecommunications Operators and Adversaries using Game Theory

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    Telecommunications systems being inherently distributed and collaborative in nature present a plurality of attack surfaces to malicious entities and hence vulnerable to many potential attacks even indirectly demanding a need in prioritising security. The choice of security implementations depends upon the currently understood threats, future possible threat vectors, and the dependencies between systems. Executing these choices while contemplating the financial aspects is exceptionally difficult. It is thus critical to have a perceptible decision support framework for better security decision-making. This thesis studies the strategic nature of the interaction between the Telecoms operators and attackers utilising game theory to understand their strategic decision-making characteristics strengthening security decisions. To understand the security investment decision-making criteria of operators, this thesis utilises static security investment games. Through these games, we study the effects of security investment decision of an operator on other operators' behaviour. We determine conditions supporting the security investment decisions and propose strategic recommendations supplementing the dependency conditions. We then study attackers' behaviour considering them with strategic incentives in contrary to their strictly-bounded rationality in traditional game-theoretic modelling approaches. We utilise a behavioural approach and design a decision-flow model capturing the choices of attackers in the attack process. An outcome of this work is a generalised attack framework. Moreover, using this framework, we derive attack strategies optimising attackers' effort. Through this work, we are probing the foundations for drawing inferences about attackers' strategic characteristics from a cybersecurity perspective
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