Optimal deployments of defense mechanisms for the internet of things

Internet of Things (IoT) devices can be exploited by the attackers as entry points to break into the IoT networks without early detection. Little work has taken hybrid approaches that combine different defense mechanisms in an optimal way to increase the security of the IoT against sophisticated attacks. In this work, we propose a novel approach to generate the strategic deployment of adaptive deception technology and the patch management solution for the IoT under a budget constraint. We use a graphical security model along with three evaluation metrics to measure the effectiveness and efficiency of the proposed defense mechanisms. We apply the multi-objective genetic algorithm (GA) to compute the {\em Pareto optimal} deployments of defense mechanisms to maximize the security and minimize the deployment cost. We present a case study to show the feasibility of the proposed approach and to provide the defenders with various ways to choose optimal deployments of defense mechanisms for the IoT. We compare the GA with the exhaustive search algorithm (ESA) in terms of the runtime complexity and performance accuracy in optimality. Our results show that the GA is much more efficient in computing a good spread of the deployments than the ESA, in proportion to the increase of the IoT devices

Multi-Quality Auto-Tuning by Contract Negotiation

A characteristic challenge of software development is the management of omnipresent change. Classically, this constant change is driven by customers changing their requirements. The wish to optimally leverage available resources opens another source of change: the software systems environment. Software is tailored to specific platforms (e.g., hardware architectures) resulting in many variants of the same software optimized for different environments. If the environment changes, a different variant is to be used, i.e., the system has to reconfigure to the variant optimized for the arisen situation. The automation of such adjustments is subject to the research community of self-adaptive systems. The basic principle is a control loop, as known from control theory. The system (and environment) is continuously monitored, the collected data is analyzed and decisions for or against a reconfiguration are computed and realized. Central problems in this field, which are addressed in this thesis, are the management of interdependencies between non-functional properties of the system, the handling of multiple criteria subject to decision making and the scalability. In this thesis, a novel approach to self-adaptive software--Multi-Quality Auto-Tuning (MQuAT)--is presented, which provides design and operation principles for software systems which automatically provide the best possible utility to the user while producing the least possible cost. For this purpose, a component model has been developed, enabling the software developer to design and implement self-optimizing software systems in a model-driven way. This component model allows for the specification of the structure as well as the behavior of the system and is capable of covering the runtime state of the system. The notion of quality contracts is utilized to cover the non-functional behavior and, especially, the dependencies between non-functional properties of the system. At runtime the component model covers the runtime state of the system. This runtime model is used in combination with the contracts to generate optimization problems in different formalisms (Integer Linear Programming (ILP), Pseudo-Boolean Optimization (PBO), Ant Colony Optimization (ACO) and Multi-Objective Integer Linear Programming (MOILP)). Standard solvers are applied to derive solutions to these problems, which represent reconfiguration decisions, if the identified configuration differs from the current. Each approach is empirically evaluated in terms of its scalability showing the feasibility of all approaches, except for ACO, the superiority of ILP over PBO and the limits of all approaches: 100 component types for ILP, 30 for PBO, 10 for ACO and 30 for 2-objective MOILP. In presence of more than two objective functions the MOILP approach is shown to be infeasible

Electro-stimulating implants for bone regeneration: parameter analysis and design optimization

This thesis investigated a bipolar induction screw system with an integrated coil for bone electrical stimulation. The aim was to analyse the influence of the stimulation parameters and electro-stimulating implants parameters on bone regeneration and carry out a parameter optimization for bone electrical stimulation. Finite element analysis was used to calculate the electric field distributions in the bone. The results showed that the screw’s z-direction positioning (moving in and out of femoral head) yields the highest effect on the volume tissue activated in patient’s femoral head model

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School

Multi-criteria optimization algorithms for high dose rate brachytherapy

L’objectif général de cette thèse est d’utiliser les connaissances en physique de la radiation, en programmation informatique et en équipement informatique à la haute pointe de la technologie pour améliorer les traitements du cancer. En particulier, l’élaboration d’un plan de traitement en radiothérapie peut être complexe et dépendant de l’utilisateur. Cette thèse a pour objectif de simplifier la planification de traitement actuelle en curiethérapie de la prostate à haut débit de dose (HDR). Ce projet a débuté à partir d’un algorithme de planification inverse largement utilisé, la planification de traitement inverse par recuit simulé (IPSA). Pour aboutir à un algorithme de planification inverse ultra-rapide et automatisé, trois algorithmes d’optimisation multicritères (MCO) ont été mis en oeuvre. Suite à la génération d’une banque de plans de traitement ayant divers compromis avec les algorithmes MCO, un plan de qualité a été automatiquement sélectionné. Dans la première étude, un algorithme MCO a été introduit pour explorer les frontières de Pareto en curiethérapie HDR. L’algorithme s’inspire de la fonctionnalité MCO intégrée au système Raystation (RaySearch Laboratories, Stockholm, Suède). Pour chaque cas, 300 plans de traitement ont été générés en série pour obtenir une approximation uniforme de la frontière de Pareto. Chaque plan optimal de Pareto a été calculé avec IPSA et chaque nouveau plan a été ajouté à la portion de la frontière de Pareto où la distance entre sa limite supérieure et sa limite inférieure était la plus grande. Dans une étude complémentaire, ou dans la seconde étude, un algorithme MCO basé sur la connaissance (kMCO) a été mis en oeuvre pour réduire le temps de calcul de l’algorithme MCO. Pour ce faire, deux stratégies ont été mises en oeuvre : une prédiction de l’espace des solutions cliniquement acceptables à partir de modèles de régression et d’un calcul parallèle des plans de traitement avec deux processeurs à six coeurs. En conséquence, une banque de plans de traitement de petite taille (14) a été générée et un plan a été sélectionné en tant que plan kMCO. L’efficacité de la planification et de la performance dosimétrique ont été comparées entre les plans approuvés par le médecin et les plans kMCO pour 236 cas. La troisième et dernière étude de cette thèse a été réalisée en coopération avec Cédric Bélanger. Un algorithme MCO (gMCO) basé sur l’utilisation d’un environnement de développement compatible avec les cartes graphiques a été mis en oeuvre pour accélérer davantage le calcul. De plus, un algorithme d’optimisation quasi-Newton a été implémenté pour remplacer le recuit simulé dans la première et la deuxième étude. De cette manière, un millier de plans de traitement avec divers compromis et équivalents à ceux générés par IPSA ont été calculés en parallèle. Parmi la banque de plans de traitement généré par l’agorithme gMCO, un plan a été sélectionné (plan gMCO). Le temps de planification et les résultats dosimétriques ont été comparés entre les plans approuvés par le médecin et les plans gMCO pour 457 cas. Une comparaison à grande échelle avec les plans approuvés par les radio-oncologues montre que notre dernier algorithme MCO (gMCO) peut améliorer l’efficacité de la planification du traitement (de quelques minutes à 9:4 s) ainsi que la qualité dosimétrique des plans de traitements (des plans passant de 92:6% à 99:8% selon les critères dosimétriques du groupe de traitement oncologique par radiation (RTOG)). Avec trois algorithmes MCO mis en oeuvre, cette thèse représente un effort soutenu pour développer un algorithme de planification inverse ultra-rapide, automatique et robuste en curiethérapie HDR.The overall purpose of this thesis is to use the knowledge of radiation physics, computer programming and computing hardware to improve cancer treatments. In particular, designing a treatment plan in radiation therapy can be complex and user-dependent, and this thesis aims to simplify current treatment planning in high dose rate (HDR) prostate brachytherapy. This project was started from a widely used inverse planning algorithm, Inverse Planning Simulated Annealing (IPSA). In order to eventually lead to an ultra-fast and automatic inverse planning algorithm, three multi-criteria optimization (MCO) algorithms were implemented. With MCO algorithms, a desirable plan was selected after computing a set of treatment plans with various trade-offs. In the first study, an MCO algorithm was introduced to explore the Pareto surfaces in HDR brachytherapy. The algorithm was inspired by the MCO feature integrated in the Raystation system (RaySearch Laboratories, Stockholm, Sweden). For each case, 300 treatment plans were serially generated to obtain a uniform approximation of the Pareto surface. Each Pareto optimal plan was computed with IPSA, and each new plan was added to the Pareto surface portion where the distance between its upper boundary and its lower boundary was the largest. In a companion study, or the second study, a knowledge-based MCO (kMCO) algorithm was implemented to shorten the computation time of the MCO algorithm. To achieve this, two strategies were implemented: a prediction of clinical relevant solution space with previous knowledge, and a parallel computation of treatment plans with two six-core CPUs. As a result, a small size (14) plan dataset was created, and one plan was selected as the kMCO plan. The planning efficiency and the dosimetric performance were compared between the physician-approved plans and the kMCO plans for 236 cases. The third and final study of this thesis was conducted in cooperation with Cédric Bélanger. A graphics processing units (GPU) based MCO (gMCO) algorithm was implemented to further speed up the computation. Furthermore, a quasi-Newton optimization engine was implemented to replace simulated annealing in the first and the second study. In this way, one thousand IPSA equivalent treatment plans with various trade-offs were computed in parallel. One plan was selected as the gMCO plan from the calculated plan dataset. The planning time and the dosimetric results were compared between the physician-approved plans and the gMCO plans for 457 cases. A large-scale comparison against the physician-approved plans shows that our latest MCO algorithm (gMCO) can result in an improved treatment planning efficiency (from minutes to 9:4 s) as well as an improved treatment plan dosimetric quality (Radiation Therapy Oncology Group (RTOG) acceptance rate from 92.6% to 99.8%). With three implemented MCO algorithms, this thesis represents a sustained effort to develop an ultra-fast, automatic and robust inverse planning algorithm in HDR brachytherapy