Protection optimale des réseaux logistiques contre des attaques

L’évolution des réseaux logistiques s'accompagne du risque inquiétant d'attaques intentionnelles et ils deviennent de plus en plus des cibles potentielles. Il est donc essentiel de les protéger contre ces attaques. Cette thèse développe une méthode d’allocation optimale des ressources de défense des entités de réseaux logistiques contre des attaques intentionnelles, dans le contexte de l’optimisation de la localisation des installations. Cette méthode permet de calculer les dégâts, d’évaluer la valeur d'une cible critique et de répartir de façon optimale des ressources limitées de défense, et ce, en tenant compte de la stratégie de l’attaquant. Trois cas de réseaux logistiques sont étudiés dans cette thèse. Nous considérons tout d’abord le contexte de localisation d’installations à capacité illimitée où une stratégie de protection optimale est sélectionnée. Ensuite, la notion de capacité supplémentaire est utilisée comme moyen de protection indirecte, permettant de réduire le dommage encouru. Enfin, comme l'efficacité d’un réseau logistique est largement déterminée par le bon fonctionnement des entrepôts, ces derniers sont également protégés de la même façon que les usines. Alors que l’objectif du défendeur est de minimiser les dégâts, l’attaquant va chercher à maximiser ces dégâts. Dans les trois cas considérés, le problème sera défini comme un jeu non coopératif min-max à deux périodes dans lequel le défendeur joue en premier. Cela signifie que le défendeur choisit une stratégie à la première période qui minimise le dégât maximal que l’attaquant pourrait causer à la seconde période. Dans le but d’estimer les utilités des joueurs, la méthode proposée évalue l’espérance mathématique de plusieurs coûts : le coût nécessaire à la restauration des installations endommagées, les coûts encourus en raison de l'augmentation possible des coûts de transport suite à des attaques, et le coût dû à une rupture de stock. Un algorithme est développé afin de déterminer la solution d'équilibre et la stratégie de défense optimale. Dans chacun des cas étudiés, notre méthode est comparée à d'autres stratégies. Les résultats obtenus montrent clairement l’efficacité de notre modèle et l’apport de la capacité supplémentaire comme moyen de protection indirecte, ainsi que l’importance de protéger les entrepôts.The evolution of logistic networks is accompanied by the worrisome risk of intentional attacks and these networks increasingly become potential targets. It is essential to protect them against these attacks. This thesis develops a method for the optimal allocation of defensive resources among the entities’ logistic networks to protect against intentional attacks in the context of facility location optimization. This method is developed to calculate the expected damage, evaluate the value of a critical target and optimally distribute the limited defensive resources, taking the attacker’s strategy into account. Three cases of logistics networks are studied in this thesis. We first of all consider the context of the uncapacitated fixed-charge location where a strategy of optimal protection is selected. Then, extra-capacity is used as a means for indirect protection, allowing the reduction of the expected damage. Lastly, as the efficiency and effectiveness in any logistics network is largely determined by the correct operation of the warehouses, the latter are also protected in the same way as the plants. Whereas the defender’s objective is to minimize the damage, the attacker seeks to maximize this damage. In the three cases considered the problem formulation as a two-period game where the defender invests in the first period. This means that the defender selects a strategy in the first period that minimizes the maximum loss the attacker may cause in the second period. A method is developed to evaluate the utilities of the players. This method evaluates many expected costs, including the cost needed to restore disabled facilities, the backorder cost, and the cost incurred because of the change in transportation costs after attacks. An algorithm is developed to find the equilibrium solution and the optimal defence strategy. Our method is compared to other suggested strategies. Obtained results clearly indicate the effectiveness of our model and the indirect protection by extra-capacity deployment, as well as the importance of protecting the warehouses

Optimal defence-attack strategies between one defender and two attackers

This paper analyses the optimal strategies for one defender and two attackers in a defence-attack game, where a) the defender allocates its resource into defending against and attacking the two attackers, and b) the two attackers, after observing the action of the defender, allocate their resources into attacking and defending against the defender, on either a cooperative or non-cooperative basis. On a cooperative basis, for each of the defender’s given strategies, the two attackers work together to maximise the sum of their cumulative prospect values while anticipating the eight possible game outcomes. On a non-cooperative basis, for each of the defender’s given strategies, each attacker simultaneously yet independently tries to maximise their own cumulative prospect value. In both cases, the defender maximises its cumulative prospect value while anticipating the attackers’ actions. Backward induction is employed to obtain the optimal defence and attack strategies for all scenarios. Numerical examples are performed to illustrate the applications of the strategies. In general, we find two opposing effects considering the attackers’ strategies and analyse the alteration of strategies for the participants under two different risk preferences: risk-averse and risk seeking. The reasons for the alteration are also performed to illustrate the practical applications

A Unified Framework for Measuring a Network's Mean Time-to-Compromise

Measuring the mean time-to-compromise provides important insights for understanding a network's weaknesses and for guiding corresponding defense approaches. Most existing network security metrics only deal with the threats of known vulnerabilities and cannot handle zero day attacks with consistent semantics. In this thesis, we propose a unified framework for measuring a network's mean time-to-compromise by considering both known, and zero day attacks. Specifically, we first devise models of the mean time for discovering and exploiting individual vulnerabilities. Unlike existing approaches, we replace the generic state transition model with a more vulnerability-specific graphical model. We then employ Bayesian networks to derive the overall mean time-to-compromise by aggregating the results of individual vulnerabilities. Finally, we demonstrate the framework's practical application to network hardening through case studies

Responsive Contingency Planning for Supply Chain Disruption Risk Mitigation

Contingent sourcing from a backup resource is an effective risk mitigation strategy under major disruptions. The production volumes and speeds of the backup resource are important protection design considerations, as they affect recovery. The objective of this dissertation is to show that cost-effective protection of existing supply networks from major disruptions result from planning appropriate volume and response speeds of a backup production facility prior to the disruptive event by considering operational aspects such as congestion that may occur at facilities. Contingency strategy are more responsive and disruption recovery periods can be shortened through such prior planning. The dissertation focuses on disruption risk arising from intelligent or pre-meditated attacks on supply facilities. An intelligent attacker has the capability to create worst case loss depending on the protection strategy of a given network. Since the attacker seeks the maximum loss and the designer tries to identify the protection scheme which minimizes this maximum loss, there exists an interdependence between attack and protection decisions. Ignoring this characteristic leads to suboptimal mitigation solutions under such disruptions. We therefore develop a mathematical model which utilizes a game theoretic framework of attack and defense involving nested optimization problems. The model is used to decide optimal selection of backup production volume and the response speeds, the facilities to build such capability within the available budget. The reallocation of demands from a disrupted facility to an undisrupted facility in a contingency strategy leads to congestion of the undisrupted facility, which may result in longer lead times and reduced throughput during disruption periods, thereby limiting the effectiveness of a contingency strategy. In the second part of the dissertation, we therefore analyze congestion effects in responsive contingency planning. The congestion cost function is modeled and integrated into the mathematical model of responsive contingency planning developed in the first part of the dissertation. The main contribution of this dissertation is that a decision tool has been developed to plan protection of an existing supply networks considering backup sourcing through gradual capacity acquisition. The solution methodology involving recursive search tree has been implemented which allows exploring protection solutions under a given budget of protection and multiple combinations of response speeds and production capacities of a backup facility. The results and analysis demonstrate the value of planning for responsive contingency in supply chains subject to risks of major disruptions and provide insights to aid managerial decision making

Perspectives on the relationship between local interactions and global outcomes in spatially explicit models of systems of interacting individuals

Understanding the behaviour of systems of interacting individuals is a key aim of much research in the social sciences and beyond, and a wide variety of modelling paradigms have been employed in pursuit of this goal. Often, systems of interest are intrinsically spatial, involving interactions that occur on a local scale or according to some specific spatial structure. However, while it is recognised that spatial factors can have a significant impact on the global behaviours exhibited by such systems, in practice, models often neglect spatial structure or consider it only in a limited way, in order to simplify interpretation and analysis. In the particular case of individual-based models used in the social sciences, a lack of consistent mathematical foundations inevitably casts doubt on the validity of research conclusions. Similarly, in game theory, the lack of a unifying framework to encompass the full variety of spatial games presented in the literature restricts the development of general results and can prevent researchers from identifying important similarities between models. In this thesis, we address these issues by examining the relationship between local interactions and global outcomes in spatially explicit models of interacting individuals from two different conceptual perspectives. First, we define and analyse a family of spatially explicit, individual-based models, identifying and explaining fundamental connections between their local and global behaviours. Our approach represents a proof of concept, suggesting that similar methods could be effective in identifying such connections in a wider range of models. Secondly, we define a general model for spatial games of search and concealment, which unites many existing games into a single framework, and we present theoretical results on its optimal strategies. Our model represents an opportunity for the development of a more broadly applicable theory of spatial games, which could facilitate progress and highlight connections within the field