235 research outputs found
Modélisation formelle des systèmes de détection d'intrusions
L’écosystème de la cybersécurité évolue en permanence en termes du nombre, de la diversité, et de la complexité des attaques. De ce fait, les outils de détection deviennent inefficaces face à certaines attaques. On distingue généralement trois types de systèmes de détection d’intrusions : détection par anomalies, détection par signatures et détection hybride. La détection par anomalies est fondée sur la caractérisation du comportement habituel du système, typiquement de manière statistique. Elle permet de détecter des attaques connues ou inconnues, mais génère aussi un très grand nombre de faux positifs. La détection par signatures permet de détecter des attaques connues en définissant des règles qui décrivent le comportement connu d’un attaquant. Cela demande une bonne connaissance du comportement de l’attaquant. La détection hybride repose sur plusieurs méthodes de détection incluant celles sus-citées. Elle présente l’avantage d’être plus précise pendant la détection. Des outils tels que Snort et Zeek offrent des langages de bas niveau pour l’expression de règles de reconnaissance d’attaques. Le nombre d’attaques potentielles étant très grand, ces bases de règles deviennent rapidement difficiles à gérer et à maintenir. De plus, l’expression de règles avec état dit stateful est particulièrement ardue pour reconnaître une séquence d’événements. Dans cette thèse, nous proposons une approche stateful basée sur les diagrammes d’état-transition algébriques (ASTDs) afin d’identifier des attaques complexes. Les ASTDs permettent de représenter de façon graphique et modulaire une spécification, ce qui facilite la maintenance et la compréhension des règles. Nous étendons la notation ASTD avec de nouvelles fonctionnalités pour représenter des attaques complexes. Ensuite, nous spécifions plusieurs attaques avec la notation étendue et exécutons les spécifications obtenues sur des flots d’événements à l’aide d’un interpréteur pour identifier des attaques. Nous évaluons aussi les performances de l’interpréteur avec des outils industriels tels que Snort et Zeek. Puis, nous réalisons un compilateur afin de générer du code exécutable à partir d’une spécification ASTD, capable d’identifier de façon efficiente les séquences d’événements.Abstract : The cybersecurity ecosystem continuously evolves with the number, the diversity,
and the complexity of cyber attacks. Generally, we have three types of Intrusion
Detection System (IDS) : anomaly-based detection, signature-based detection, and
hybrid detection. Anomaly detection is based on the usual behavior description of
the system, typically in a static manner. It enables detecting known or unknown attacks
but also generating a large number of false positives. Signature based detection
enables detecting known attacks by defining rules that describe known attacker’s behavior.
It needs a good knowledge of attacker behavior. Hybrid detection relies on
several detection methods including the previous ones. It has the advantage of being
more precise during detection. Tools like Snort and Zeek offer low level languages to
represent rules for detecting attacks. The number of potential attacks being large,
these rule bases become quickly hard to manage and maintain. Moreover, the representation
of stateful rules to recognize a sequence of events is particularly arduous. In this thesis, we propose a stateful approach based on algebraic state-transition
diagrams (ASTDs) to identify complex attacks. ASTDs allow a graphical and modular
representation of a specification, that facilitates maintenance and understanding of
rules. We extend the ASTD notation with new features to represent complex attacks.
Next, we specify several attacks with the extended notation and run the resulting specifications
on event streams using an interpreter to identify attacks. We also evaluate
the performance of the interpreter with industrial tools such as Snort and Zeek. Then,
we build a compiler in order to generate executable code from an ASTD specification,
able to efficiently identify sequences of events
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Applications
Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications
Trajectory planning based on adaptive model predictive control: Study of the performance of an autonomous vehicle in critical highway scenarios
Increasing automation in automotive industry is an important contribution to
overcome many of the major societal challenges. However, testing and validating a highly
autonomous vehicle is one of the biggest obstacles to the deployment of such vehicles,
since they rely on data-driven and real-time sensors, actuators, complex algorithms,
machine learning systems, and powerful processors to execute software, and they must
be proven to be reliable and safe.
For this reason, the verification, validation and testing (VVT) of autonomous
vehicles is gaining interest and attention among the scientific community and there has
been a number of significant efforts in this field. VVT helps developers and testers to
determine any hidden faults, increasing systems confidence in safety, security, functional
analysis, and in the ability to integrate autonomous prototypes into existing road
networks. Other stakeholders like higher-management, public authorities and the public
are also crucial to complete the VTT process.
As autonomous vehicles require hundreds of millions of kilometers of testing
driven on public roads before vehicle certification, simulations are playing a key role as
they allow the simulation tools to virtually test millions of real-life scenarios, increasing
safety and reducing costs, time and the need for physical road tests.
In this study, a literature review is conducted to classify approaches for the VVT
and an existing simulation tool is used to implement an autonomous driving system. The
system will be characterized from the point of view of its performance in some critical
highway scenarios.O aumento da automação na indústria automotiva é uma importante
contribuição para superar muitos dos principais desafios da sociedade. No entanto,
testar e validar um veĂculo altamente autĂłnomo Ă© um dos maiores obstáculos para a
implantação de tais veĂculos, uma vez que eles contam com sensores, atuadores,
algoritmos complexos, sistemas de aprendizagem de máquina e processadores potentes
para executar softwares em tempo real, e devem ser comprovadamente confiáveis e
seguros.
Por esta razĂŁo, a verificação, validação e teste (VVT) de veĂculos autĂłnomos está
a ganhar interesse e atenção entre a comunidade cientĂfica e tem havido uma sĂ©rie de
esforços significativos neste campo. A VVT ajuda os desenvolvedores e testadores a
determinar quaisquer falhas ocultas, aumentando a confiança dos sistemas na
segurança, proteção, análise funcional e na capacidade de integrar protótipos autónomos
em redes rodoviárias existentes. Outras partes interessadas, como a alta administração,
autoridades públicas e o público também são cruciais para concluir o processo de VTT.
Como os veĂculos autĂłnomos exigem centenas de milhões de quilĂłmetros de
testes conduzidos em vias pĂşblicas antes da certificação do veĂculo, as simulações estĂŁo
a desempenhar cada vez mais um papel fundamental, pois permitem que as ferramentas
de simulação testem virtualmente milhões de cenários da vida real, aumentando a
segurança e reduzindo custos, tempo e necessidade de testes fĂsicos em estrada.
Neste estudo, Ă© realizada uma revisĂŁo da literatura para classificar abordagens
para a VVT e uma ferramenta de simulação existente é usada para implementar um
sistema de direção autónoma. O sistema é caracterizado do ponto de vista do seu
desempenho em alguns cenários crĂticos de autoestrad
D4.2 Intelligent D-Band wireless systems and networks initial designs
This deliverable gives the results of the ARIADNE project's Task 4.2: Machine Learning based network intelligence. It presents the work conducted on various aspects of network management to deliver system level, qualitative solutions that leverage diverse machine learning techniques. The different chapters present system level, simulation and algorithmic models based on multi-agent reinforcement learning, deep reinforcement learning, learning automata for complex event forecasting, system level model for proactive handovers and resource allocation, model-driven deep learning-based channel estimation and feedbacks as well as strategies for deployment of machine learning based solutions. In short, the D4.2 provides results on promising AI and ML based methods along with their limitations and potentials that have been investigated in the ARIADNE project
Shaping future low-carbon energy and transportation systems: Digital technologies and applications
Digitalization and decarbonization are projected to be two major trends in the coming decades. As the already widespread process of digitalization continues to progress, especially in energy and transportation systems, massive data will be produced, and how these data could support and promote decarbonization has become a pressing concern. This paper presents a comprehensive review of digital technologies and their potential applications in low-carbon energy and transportation systems from the perspectives of infrastructure, common mechanisms and algorithms, and system-level impacts, as well as the application of digital technologies to coupled energy and transportation systems with electric vehicles. This paper also identifies corresponding challenges and future research directions, such as in the field of blockchain, digital twin, vehicle-to-grid, low-carbon computing, and data security and privacy, especially in the context of integrated energy and transportation systems
Motion Planning of UAV Swarm: Recent Challenges and Approaches
The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work
Evidence-based Development of Trustworthy Mobile Medical Apps
abstract: Widespread adoption of smartphone based Mobile Medical Apps (MMAs) is opening new avenues for innovation, bringing MMAs to the forefront of low cost healthcare delivery. These apps often control human physiology and work on sensitive data. Thus it is necessary to have evidences of their trustworthiness i.e. maintaining privacy of health data, long term operation of wearable sensors and ensuring no harm to the user before actual marketing. Traditionally, clinical studies are used to validate the trustworthiness of medical systems. However, they can take long time and could potentially harm the user. Such evidences can be generated using simulations and mathematical analysis. These methods involve estimating the MMA interactions with human physiology. However, the nonlinear nature of human physiology makes the estimation challenging.
This research analyzes and develops MMA software while considering its interactions with human physiology to assure trustworthiness. A novel app development methodology is used to objectively evaluate trustworthiness of a MMA by generating evidences using automatic techniques. It involves developing the Health-Dev β tool to generate a) evidences of trustworthiness of MMAs and b) requirements assured code generation for vulnerable components of the MMA without hindering the app development process. In this method, all requests from MMAs pass through a trustworthy entity, Trustworthy Data Manager which checks if the app request satisfies the MMA requirements. This method is intended to expedite the design to marketing process of MMAs. The objectives of this research is to develop models, tools and theory for evidence generation and can be divided into the following themes:
• Sustainable design configuration estimation of MMAs: Developing an optimization framework which can generate sustainable and safe sensor configuration while considering interactions of the MMA with the environment.
• Evidence generation using simulation and formal methods: Developing models and tools to verify safety properties of the MMA design to ensure no harm to the human physiology.
• Automatic code generation for MMAs: Investigating methods for automatically
• Performance analysis of trustworthy data manager: Evaluating response time generating trustworthy software for vulnerable components of a MMA and evidences.performance of trustworthy data manager under interactions from non-MMA smartphone apps.Dissertation/ThesisDoctoral Dissertation Computer Science 201
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