Perfomance Analysis and Resource Optimisation of Critical Systems Modelled by Petri Nets

Un sistema crítico debe cumplir con su misión a pesar de la presencia de problemas de seguridad. Este tipo de sistemas se suele desplegar en entornos heterogéneos, donde pueden ser objeto de intentos de intrusión, robo de información confidencial u otro tipo de ataques. Los sistemas, en general, tienen que ser rediseñados después de que ocurra un incidente de seguridad, lo que puede conducir a consecuencias graves, como el enorme costo de reimplementar o reprogramar todo el sistema, así como las posibles pérdidas económicas. Así, la seguridad ha de ser concebida como una parte integral del desarrollo de sistemas y como una necesidad singular de lo que el sistema debe realizar (es decir, un requisito no funcional del sistema). Así pues, al diseñar sistemas críticos es fundamental estudiar los ataques que se pueden producir y planificar cómo reaccionar frente a ellos, con el fin de mantener el cumplimiento de requerimientos funcionales y no funcionales del sistema. A pesar de que los problemas de seguridad se consideren, también es necesario tener en cuenta los costes incurridos para garantizar un determinado nivel de seguridad en sistemas críticos. De hecho, los costes de seguridad puede ser un factor muy relevante ya que puede abarcar diferentes dimensiones, como el presupuesto, el rendimiento y la fiabilidad. Muchos de estos sistemas críticos que incorporan técnicas de tolerancia a fallos (sistemas FT) para hacer frente a las cuestiones de seguridad son sistemas complejos, que utilizan recursos que pueden estar comprometidos (es decir, pueden fallar) por la activación de los fallos y/o errores provocados por posibles ataques. Estos sistemas pueden ser modelados como sistemas de eventos discretos donde los recursos son compartidos, también llamados sistemas de asignación de recursos. Esta tesis se centra en los sistemas FT con recursos compartidos modelados mediante redes de Petri (Petri nets, PN). Estos sistemas son generalmente tan grandes que el cálculo exacto de su rendimiento se convierte en una tarea de cálculo muy compleja, debido al problema de la explosión del espacio de estados. Como resultado de ello, una tarea que requiere una exploración exhaustiva en el espacio de estados es incomputable (en un plazo prudencial) para sistemas grandes. Las principales aportaciones de esta tesis son tres. Primero, se ofrecen diferentes modelos, usando el Lenguaje Unificado de Modelado (Unified Modelling Language, UML) y las redes de Petri, que ayudan a incorporar las cuestiones de seguridad y tolerancia a fallos en primer plano durante la fase de diseño de los sistemas, permitiendo así, por ejemplo, el análisis del compromiso entre seguridad y rendimiento. En segundo lugar, se proporcionan varios algoritmos para calcular el rendimiento (también bajo condiciones de fallo) mediante el cálculo de cotas de rendimiento superiores, evitando así el problema de la explosión del espacio de estados. Por último, se proporcionan algoritmos para calcular cómo compensar la degradación de rendimiento que se produce ante una situación inesperada en un sistema con tolerancia a fallos

Operation of Modular Smart Grid Applications Interacting through a Distributed Middleware

IoT-functionality can broaden the scope of distribution system automation in terms of functionality and communication. However, it also poses risks regarding resource consumption and security. This article presents a field approved IoT-enabled smart grid middleware, which allows for flexible deployment and management of applications within smart grid operation. In the first part of the work, the resource consumption of the middleware is analyzed and current memory bottlenecks are identified. The bottlenecks can be resolved by introducing a new entity that allows to dynamically load multiple applications within one JVM. The performance was experimentally tested and the results suggest that its application can significantly reduce the applications' memory footprint on the physical device. The second part of the study identifies and discusses potential security threats, with a focus on attacks stemming from malicious software applications within the framework. In order to prevent such attacks a proxy based prevention mechanism is developed and demonstrated

Secure Virtualization and Multicore Platforms State-of-the-Art report

SVaM

Détecter et survivre aux intrusions : exploration de nouvelles approches de détection, de restauration, et de réponse aux intrusions

Computing platforms, such as embedded systems or laptops, are built with layers of preventive security mechanisms to reduce the likelihood of attackers successfully compromising them. Nevertheless, given time and despite decades of improvements in preventive security, intrusions still happen. Therefore, systems should expect intrusions to occur, thus they should be built to detect and to survive them. Commodity Operating Systems (OSs) are deployed with intrusion detection solutions, but their ability to survive them is limited. State-of-the-art approaches from industry or academia either involve manual procedures, loss of availability, coarse-grained responses, or non-negligible performance overhead. Moreover, low-level components, such as the BIOS, are increasingly targeted by sophisticated attackers to implant stealthy and resilient malware. State-of-the-art solutions, however, mainly focus on boot time integrity, leaving the runtime part of the BIOS—known as the System Management Mode (SMM)—a prime target. This dissertation shows that we can build platforms that detect intrusions at the BIOS level and survive intrusions at the OS level. First, by demonstrating that intrusion survivability is a viable approach for commodity OSs. We develop a new approach that address various limitations from the literature, and we evaluate its security and performance. Second, by developing a hardware-based approach that detects attacks at the BIOS level where we demonstrate its feasibility with multiple detection methods.Les systèmes informatiques, tels que les ordinateurs portables ou les systèmes embarqués, sont construits avec des couches de mécanismes de sécurité préventifs afin de réduire la probabilité qu'un attaquant les compromettent. Néanmoins, malgré des décennies d'avancées dans ce domaine, des intrusions surviennent toujours. Par conséquent, nous devons supposer que des intrusions auront lieu et nous devons construire nos systèmes afin qu'ils puissent les détecter et y survivre. Les systèmes d'exploitation généralistes sont déployés avec des mécanismes de détection d'intrusion, mais leur capacité à survivre à une intrusion est limitée. Les solutions de l'état de l'art nécessitent des procédures manuelles, comportent des pertes de disponibilité, ou font subir un fort coût en performance. De plus, les composants de bas niveau tels que le BIOS sont de plus en plus la cible d'attaquants cherchant à implanter des logiciels malveillants, furtifs, et résilients. Bien que des solutions de l'état de l'art garantissent l'intégrité de ces composants au démarrage, peu s'intéressent à la sécurité des services fournis par le BIOS qui sont exécutés au sein du System Management Mode (SMM). Ce manuscrit montre que nous pouvons construire des systèmes capables de détecter des intrusions au niveau du BIOS et y survivre au niveau du système d'exploitation. Tout d'abord, nous démontrons qu'une approche de survivabilité aux intrusions est viable et praticable pour des systèmes d'exploitation généralistes. Ensuite, nous démontrons qu'il est possible de détecter des intrusions au niveau du BIOS avec une solution basée sur du matériel

Improving intrusion detection systems using data mining techniques

Recent surveys and studies have shown that cyber-attacks have caused a lot of damage to organisations, governments, and individuals around the world. Although developments are constantly occurring in the computer security field, cyber-attacks still cause damage as they are developed and evolved by hackers. This research looked at some industrial challenges in the intrusion detection area. The research identified two main challenges; the first one is that signature-based intrusion detection systems such as SNORT lack the capability of detecting attacks with new signatures without human intervention. The other challenge is related to multi-stage attack detection, it has been found that signature-based is not efficient in this area. The novelty in this research is presented through developing methodologies tackling the mentioned challenges. The first challenge was handled by developing a multi-layer classification methodology. The first layer is based on decision tree, while the second layer is a hybrid module that uses two data mining techniques; neural network, and fuzzy logic. The second layer will try to detect new attacks in case the first one fails to detect. This system detects attacks with new signatures, and then updates the SNORT signature holder automatically, without any human intervention. The obtained results have shown that a high detection rate has been obtained with attacks having new signatures. However, it has been found that the false positive rate needs to be lowered. The second challenge was approached by evaluating IP information using fuzzy logic. This approach looks at the identity of participants in the traffic, rather than the sequence and contents of the traffic. The results have shown that this approach can help in predicting attacks at very early stages in some scenarios. However, it has been found that combining this approach with a different approach that looks at the sequence and contents of the traffic, such as event- correlation, will achieve a better performance than each approach individually

Data Analysis Methods for Software Systems

Using statistics, econometrics, machine learning, and functional data analysis methods, we evaluate the consequences of the lockdown during the COVID-19 pandemics for wage inequality and unemployment. We deduce that these two indicators mostly reacted to the first lockdown from March till June 2020. Also, analysing wage inequality, we conduct analysis separately for males and females and different age groups.We noticed that young females were affected mostly by the lockdown.Nevertheless, all the groups reacted to the lockdown at some level

IntelliFlow : um enfoque proativo para adicionar inteligência de ameaças cibernéticas a redes definidas por software

Orientador: Christian Rodolfo Esteve RothenbergDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Segurança tem sido uma das principais preocupações enfrentadas pela computação em rede principalmente, com o aumento das ameaças à medida que a Internet comercial e economias afins crescem rapidamente. Tecnologias de virtualização que permitem serviços em nuvem em escala colocam novos desafios para a segurança das infraestruturas computacionais, exigindo novos mecanismos que combinem o best-of-breed para reagir contra as metodologias de ataque emergentes. Nosso trabalho busca explorar os avanços na Cyber Threat Intelligence (CTI) no contexto da arquitetura de redes definidas por software, ou em inglês, Software Defined Networking (SDN). Enquanto a CTI representa uma abordagem recente para o combate de ameaças baseada em fontes confiáveis, a partir do compartihamento de informação e conhecimento sobre atividades criminais virtuais, a SDN é uma tendência recente na arquitetura de redes computacionais baseada em princípios de modulação e programabilidade. Nesta dissertação, nós propomos IntelliFlow, um sistema de detecção de inteligência para SDN que segue a abordagem proativa usando OpenFlow para efetivar contramedidas para as ameaças aprendidas a partir de um plano de inteligência distribuida. Nós mostramos a partir de uma implementação de prova de conceito que o sistema proposto é capaz de trazer uma série de benefícios em termos de efetividade e eficiência, contribuindo no plano geral para a segurança de projetos de computação de rede modernosAbstract: Security is a major concern in computer networking which faces increasing threats as the commercial Internet and related economies continue to grow. Virtualization technologies enabling scalable Cloud services pose further challenges to the security of computer infrastructures, demanding novel mechanisms combining the best-of-breed to counter certain types of attacks. Our work aims to explore advances in Cyber Threat Intelligence (CTI) in the context of Software Defined Networking (SDN) architectures. While CTI represents a recent approach to combat threats based on reliable sources, by sharing information and knowledge about computer criminal activities, SDN is a recent trend in architecting computer networks based on modularization and programmability principles. In this dissertation, we propose IntelliFlow, an intelligent detection system for SDN that follows a proactive approach using OpenFlow to deploy countermeasures to the threats learned through a distributed intelligent plane. We show through a proof of concept implementation that the proposed system is capable of delivering a number of benefits in terms of effectiveness and efficiency, altogether contributing to the security of modern computer network designsMestradoEngenharia de ComputaçãoMestre em Engenharia Elétrica159905/2013-3CNP

Architectural Optimization for Confidentiality Under Structural Uncertainty

More and more connected systems gather and exchange data. This allows building smarter, more efficient and overall better systems. However, the exchange of data also leads to questions regarding the confidentiality of these systems. Design notions such as Security by Design or Privacy by Design help to build secure and confidential systems by considering confidentiality already at the design-time. During the design-time, different analyses can support the architect. However, essential properties that impact confidentiality, such as the deployment, might be unknown during the design-time, leading to structural uncertainty about the architecture and its confidentiality. Structural uncertainty in the software architecture represents unknown properties about the structure of the software architecture. This can be, for instance, the deployment or the actual implementation of a component. For handling this uncertainty, we combine a design space exploration and optimization approach with a dataflow-based confidentiality analysis. This helps to estimate the confidentiality of an architecture under structural uncertainty. We evaluated our approach on four application examples. The results indicate a high accuracy regarding the found confidentiality violations

Automating Cyber Analytics

Model based security metrics are a growing area of cyber security research concerned with measuring the risk exposure of an information system. These metrics are typically studied in isolation, with the formulation of the test itself being the primary finding in publications. As a result, there is a flood of metric specifications available in the literature but a corresponding dearth of analyses verifying results for a given metric calculation under different conditions or comparing the efficacy of one measurement technique over another. The motivation of this thesis is to create a systematic methodology for model based security metric development, analysis, integration, and validation. In doing so we hope to fill a critical gap in the way we view and improve a system’s security. In order to understand the security posture of a system before it is rolled out and as it evolves, we present in this dissertation an end to end solution for the automated measurement of security metrics needed to identify risk early and accurately. To our knowledge this is a novel capability in design time security analysis which provides the foundation for ongoing research into predictive cyber security analytics. Modern development environments contain a wealth of information in infrastructure-as-code repositories, continuous build systems, and container descriptions that could inform security models, but risk evaluation based on these sources is ad-hoc at best, and often simply left until deployment. Our goal in this work is to lay the groundwork for security measurement to be a practical part of the system design, development, and integration lifecycle. In this thesis we provide a framework for the systematic validation of the existing security metrics body of knowledge. In doing so we endeavour not only to survey the current state of the art, but to create a common platform for future research in the area to be conducted. We then demonstrate the utility of our framework through the evaluation of leading security metrics against a reference set of system models we have created. We investigate how to calibrate security metrics for different use cases and establish a new methodology for security metric benchmarking. We further explore the research avenues unlocked by automation through our concept of an API driven S-MaaS (Security Metrics-as-a-Service) offering. We review our design considerations in packaging security metrics for programmatic access, and discuss how various client access-patterns are anticipated in our implementation strategy. Using existing metric processing pipelines as reference, we show how the simple, modular interfaces in S-MaaS support dynamic composition and orchestration. Next we review aspects of our framework which can benefit from optimization and further automation through machine learning. First we create a dataset of network models labeled with the corresponding security metrics. By training classifiers to predict security values based only on network inputs, we can avoid the computationally expensive attack graph generation steps. We use our findings from this simple experiment to motivate our current lines of research into supervised and unsupervised techniques such as network embeddings, interaction rule synthesis, and reinforcement learning environments. Finally, we examine the results of our case studies. We summarize our security analysis of a large scale network migration, and list the friction points along the way which are remediated by this work. We relate how our research for a large-scale performance benchmarking project has influenced our vision for the future of security metrics collection and analysis through dev-ops automation. We then describe how we applied our framework to measure the incremental security impact of running a distributed stream processing system inside a hardware trusted execution environment