Project BeARCAT : Baselining, Automation and Response for CAV Testbed Cyber Security : Connected Vehicle & Infrastructure Security Assessment

Connected, software-based systems are a driver in advancing the technology of transportation systems. Advanced automated and autonomous vehicles, together with electrification, will help reduce congestion, accidents and emissions. Meanwhile, vehicle manufacturers see advanced technology as enhancing their products in a competitive market. However, as many decades of using home and enterprise computer systems have shown, connectivity allows a system to become a target for criminal intentions. Cyber-based threats to any system are a problem; in transportation, there is the added safety implication of dealing with moving vehicles and the passengers within

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

A hybrid methodology to assess cyber resilience of IoT in energy management and connected sites

Cyber threats and vulnerabilities present an increasing risk to the safe and frictionless execution of business operations. Bad actors (“hackers”), including state actors, are increasingly targeting the operational technologies (OTs) and industrial control systems (ICSs) used to protect critical national infrastructure (CNI). Minimisations of cyber risk, attack surfaces, data immutability, and interoperability of IoT are some of the main challenges of today’s CNI. Cyber security risk assessment is one of the basic and most important activities to identify and quantify cyber security threats and vulnerabilities. This research presents a novel i-TRACE security-by-design CNI methodology that encompasses CNI key performance indicators (KPIs) and metrics to combat the growing vicarious nature of remote, well-planned, and well-executed cyber-attacks against CNI, as recently exemplified in the current Ukraine conflict (2014–present) on both sides. The proposed methodology offers a hybrid method that specifically identifies the steps required (typically undertaken by those responsible for detecting, deterring, and disrupting cyber attacks on CNI). Furthermore, we present a novel, advanced, and resilient approach that leverages digital twins and distributed ledger technologies for our chosen i-TRACE use cases of energy management and connected sites. The key steps required to achieve the desired level of interoperability and immutability of data are identified, thereby reducing the risk of CNI-specific cyber attacks and minimising the attack vectors and surfaces. Hence, this research aims to provide an extra level of safety for CNI and OT human operatives, i.e., those tasked with and responsible for detecting, deterring, disrupting, and mitigating these cyber-attacks. Our evaluations and comparisons clearly demonstrate that i-TRACE has significant intrinsic advantages compared to existing “state-of-the-art” mechanisms

Derivation of a methodology to compare C,B and R detection capability in urban events

Many comparisons have been made between Chemical detectors (C), between Biological (B) detectors, and between Radiological detectors (R), providing insights to the best C, B and R equipment for a given purpose. However, no comparison has been made between C, B and R systems to appraise how C, B and R detectors perform against each other and where capability gaps lie. The dissertation generates a method to achieve an inter-comparison between C, B and R detection capabilities and identifies where to invest resources to achieve a more effective overall CBR detection architecture. The inter-comparison methodology is based on an operational analysis tool (SMARTS). The overall CBR detection architecture is illustrated through detect to warn and detect to treat mechanisms across the timeline of a realistic scenario. The scenario has been created to be non-prejudicial to C, B or R incidents, deconstructed into four frames to accommodate SMARTS. The most suitable deconstruction is into early warning, personnel security screening, initial response and definitive identification frames. The most suitable detector Key Performance Characteristics (KPCs) are identified for each frame. SMARTS is performed by analysing the current performance of the C, B and R detection systems drawn from the literature and the target requirements determined by defensible logic. The desire to improve each capability from its current state to target requirement is subjectively determined by the author. A sensitivity analysis is applied to mitigate the effect of a limited pool of opinion. Applying the methodology to published CBR detection capability data and the author’s appraisal of the target requirement reveals that B detection requires the greatest development and R the least, and that detection in the security screening and initial response frames falls short of capability compared to early warning and definitive identification frames. Selectivity is a challenge across a broad range of frames and agents. This work provides a methodology that is modular and transparent so that it can be repopulated should new data or alternative perception arises

Gaining cyber security insight through an analysis of open source intelligence data: an East African case study

With each passing year the number of Internet users and connected devices grows, and this is particularly so in Africa. This growth brings with it an increase in the prevalence cyber-attacks. Looking at the current state of affairs, cybersecurity incidents are more likely to increase in African countries mainly due to the increased prevalence and affordability of broadband connectivity which is coupled with lack of online security awareness. The adoption of mobile banking has aggravated the situation making the continent more attractive to hackers who bank on the malpractices of users. Using Open Source Intelligence (OSINT) data sources like Sentient Hvper-Optimised Data Access Network (SHODAN) and Internet Background Radiation (IBR), this research explores the prevalence of vulnerabilities and their accessibility to evber threat actors. The research focuses on the East African Community (EAC) comprising of Tanzania, Kenya, Malawi, and Uganda, An IBR data set collected by a Rhodes University network telescope spanning over 72 months was used in this research, along with two snapshot period of data from the SHODAN project. The findings shows that there is a significant risk to systems within the EAC, particularly using the SHODAN data. The MITRE CVSS threat scoring system was applied to this research using FREAK and Heartbleed as sample vulnerabilities identified in EAC, When looking at IBR, the research has shown that attackers can use either destination ports or IP source addresses to perform an attack which if not attended to may be reused yearly until later on move to the allocated IP address space once it starts making random probes. The moment it finds one vulnerable client on the network it spreads throughout like a worm, DDoS is one the attacks that can be generated from IBR, Since the SHODAN dataset had two collection points, the study has shown the changes that have occurred in Malawi and Tanzania for a period of 14 months by using three variables i.e, device type, operating systems, and ports. The research has also identified vulnerable devices in all the four countries. Apart from that, the study identified operating systems, products, OpenSSL, ports and ISPs as some of the variables that can be used to identify vulnerabilities in systems. In the ease of OpenSSL and products, this research went further by identifying the type of attack that can occur and its associated CVE-ID

Game-theoretic APT defense: an experimental study on robotics

This paper proposes a novel game-theoretic framework for defending against Advanced Persistent Threats (APTs). It applies the original Cut-The-Rope model into an experimental study extending the previously studied attacker movements beyond the Poisson distribution to a realistic set of attack actions. More importantly, it demonstrates the value of this framework on an experimental study of an APT defense game on attack graphs, which lets a security officer establish an optimized defense policy against stealthy intrusions. The security model and algorithm under study is designed for practical use with attack graphs as threat models, possibly including vulnerability information if available. The game-theoretic optimization delivers a proactive defense policy under the following assumptions or requirements: first, we do not need to assume that the system is, or has been, clean from adversaries at any time. At the moment when the defender computes the defense policy, the attacker is assumed to already be in the system (also having penetrated it until an unknown depth). Second, the defender does not rely on any signaling or other indicators of adversarial activity, nor is there a reliable feedback mechanism to tell the defender if its actions were successful or not. Third, the model can use information on exploits, such as Common Vulnerabilities and Exposures (CVE) numbers, to refine the defense game, but can also operate without such information. We corroborate our findings on publicly documented attack graphs from the robotics domain; without and with CVE information. We run experiments against two different types of defense regimes, and compare the results against an intuitive baseline defense heuristic. The results show that the optimized defense strongly outperforms simple heuristics, like taking the shortest or easiest attack paths

Cyber-Physical Threat Intelligence for Critical Infrastructures Security

Modern critical infrastructures comprise of many interconnected cyber and physical assets, and as such are large scale cyber-physical systems. Hence, the conventional approach of securing these infrastructures by addressing cyber security and physical security separately is no longer effective. Rather more integrated approaches that address the security of cyber and physical assets at the same time are required. This book presents integrated (i.e. cyber and physical) security approaches and technologies for the critical infrastructures that underpin our societies. Specifically, it introduces advanced techniques for threat detection, risk assessment and security information sharing, based on leading edge technologies like machine learning, security knowledge modelling, IoT security and distributed ledger infrastructures. Likewise, it presets how established security technologies like Security Information and Event Management (SIEM), pen-testing, vulnerability assessment and security data analytics can be used in the context of integrated Critical Infrastructure Protection. The novel methods and techniques of the book are exemplified in case studies involving critical infrastructures in four industrial sectors, namely finance, healthcare, energy and communications. The peculiarities of critical infrastructure protection in each one of these sectors is discussed and addressed based on sector-specific solutions. The advent of the fourth industrial revolution (Industry 4.0) is expected to increase the cyber-physical nature of critical infrastructures as well as their interconnection in the scope of sectorial and cross-sector value chains. Therefore, the demand for solutions that foster the interplay between cyber and physical security, and enable Cyber-Physical Threat Intelligence is likely to explode. In this book, we have shed light on the structure of such integrated security systems, as well as on the technologies that will underpin their operation. We hope that Security and Critical Infrastructure Protection stakeholders will find the book useful when planning their future security strategies

Optimisation de la réponse aux menaces basée sur les coûts dans des systèmes pour la Sécurité de l'Information et la Gestion des Evénements (SIEMs)

Les SIEMs (systèmes pour la Sécurité de l'Information et la Gestion des Evénements) sont le cœur des centres opérationnels de sécurité actuels. Les SIEMs corrèlent les événements en provenance de différents capteurs (anti-virus, pare-feux, systèmes de détection d'intrusion, etc), et offrent des vues synthétiques pour la gestion des menaces ainsi que des rapports de sécurité. La recherche dans les technologies SIEM a toujours mis l'accent sur la fourniture d'une interprétation complète des menaces, en particulier pour évaluer leur importance et hiérarchiser les réponses. Toutefois, dans de nombreux cas, la réponse des menaces a encore besoin de l'homme pour mener l'analyse et aboutir à la prise de décisions, p.ex. compréhension des menaces, définition des contremesures appropriées ainsi que leur déploiement. Il s'agit d'un processus lent et coûteux, nécessitant un haut niveau d'expertise, qui reste néanmoins sujet à erreurs. Ainsi, des recherches récentes sur les SIEMs ont mis l'accent sur l'importance et la capacité d'automatiser le processus de sélection et le déploiement des contremesures. Certains auteurs ont proposé des mécanismes automatiques de réponse, comme l'adaptation des politiques de sécurité pour dépasser les limites de réponses statiques ou manuelles. Bien que ces approches améliorent le processus de réaction (en le rendant plus rapide et/ou plus efficace), ils restent limités car ces solutions n'analysent pas l'impact des contremesures choisies pour atténuer les attaques. Dans cette thèse, nous proposons une nouvelle approche systématique qui sélectionne la contremesure optimale au travers d'un ensemble de candidats, classés sur la base d'une comparaison entre leur efficacité à arrêter l'attaque et leur capacité à préserver, simultanément, le meilleur service aux utilisateurs légitimes. Nous proposons également un modèle pour représenter graphiquement les attaques et les contre-mesures, afin de déterminer le volume de chaque élément dans un scénario de multiples attaques. Les coordonnées de chaque élément sont dérivés d'un URI . Ce dernier est composé principalement de trois axes : l utilisateur, le canal et le ressource. Nous utilisons la méthodologie CARVER pour donner un poids approprié à chaque élément composant les axes de notre système de coordonnées. Cette approche nous permet de connecter les volumes avec les risques (p.ex. des grands volumes sont équivalents à des risques élevés, tandis que des petits volumes sont équivalents à des risques faibles). Deux concepts sont considérés en comparant deux ou plusieurs volumes de risques: le risque résiduel, qui résulte lorsque le volume du risque est plus élevé que le volume de la contre-mesure, et le dommage collatéral, qui en résulte lorsque le volume de la contre-mesure est supérieur au volume du risque. En conséquence, nous sommes en mesure d'évaluer les contre-mesures pour des scénarios d'attaques individuelles et multiples, ce qui permet de sélectionner la contre-mesure ou groupe de contre-mesures qui fournit le plus grand bénéfice à l'organisationCurrent Security Information and Event Management systems (SIEMs) constitute the central platform of modern security operating centers. They gather events from various sensors (intrusion detection systems, anti-virus, firewalls, etc.), correlate these events, and deliver synthetic views for threat handling and security reporting. Research in SIEM technologies has traditionally focused on providing a comprehensive interpretation of threats, in particular to evaluate their importance and prioritize responses accordingly. However, in many cases, threat responses still require humans to carry out the analysis and decision tasks e.g., understanding the threats, defining the appropriate countermeasures and deploying them. This is a slow and costly process, requiring a high level of expertise, and remaining error-prone nonetheless. Thus, recent research in SIEM technology has focused on the ability to automate the process of selecting and deploying countermeasures. Several authors have proposed automatic response mechanisms, such as the adaptation of security policies, to overcome the limitations of static or manual response. Although these approaches improve the reaction process (making it faster and/or more efficient), they remain limited since these solutions do not analyze the impact of the countermeasures selected to mitigate the attacks. In this thesis, we propose a novel and systematic process to select the optimal countermeasure from a pool of candidates, by ranking them based on a trade-off between their efficiency in stopping the attack and their ability to preserve, at the same time, the best service to normal users. In addition, we propose a model to represent graphically attacks and countermeasures, so as to determine the volume of each element in a scenario of multiple attacks. The coordinates of each element are derived from a URI. This latter is mainly composed of three axes: user, channel, and resource. We use the CARVER methodology to give an appropriate weight to each element composing the axes in our coordinate system. This approach allows us to connect the volumes with the risks (i.e. big volumes are equivalent to high risk, whereas small volumes are equivalent to low risk). Two concepts are considered while comparing two or more risk volumes: Residual risk, which results when the risk volume is higher than the countermeasure volume; and Collateral damage, which results when the countermeasure volume is higher than the risk volume. As a result, we are able to evaluate countermeasures for single and multiple attack scenarios, making it possible to select the countermeasure or group of countermeasures that provides the highest benefit to the organizationEVRY-INT (912282302) / SudocSudocFranceF

Identity Management and Authorization Infrastructure in Secure Mobile Access to Electronic Health Records

We live in an age of the mobile paradigm of anytime/anywhere access, as the mobile device is the most ubiquitous device that people now hold. Due to their portability, availability, easy of use, communication, access and sharing of information within various domains and areas of our daily lives, the acceptance and adoption of these devices is still growing. However, due to their potential and raising numbers, mobile devices are a growing target for attackers and, like other technologies, mobile applications are still vulnerable. Health information systems are composed with tools and software to collect, manage, analyze and process medical information (such as electronic health records and personal health records). Therefore, such systems can empower the performance and maintenance of health services, promoting availability, readability, accessibility and data sharing of vital information about a patients overall medical history, between geographic fragmented health services. Quick access to information presents a great importance in the health sector, as it accelerates work processes, resulting in better time utilization. Additionally, it may increase the quality of care. However health information systems store and manage highly sensitive data, which raises serious concerns regarding patients privacy and safety, and may explain the still increasing number of malicious incidents reports within the health domain. Data related to health information systems are highly sensitive and subject to severe legal and regulatory restrictions, that aim to protect the individual rights and privacy of patients. Along side with these legislations, security requirements must be analyzed and measures implemented. Within the necessary security requirements to access health data, secure authentication, identity management and access control are essential to provide adequate means to protect data from unauthorized accesses. However, besides the use of simple authentication models, traditional access control models are commonly based on predefined access policies and roles, and are inflexible. This results in uniform access control decisions through people, different type of devices, environments and situational conditions, and across enterprises, location and time. Although already existent models allow to ensure the needs of the health care systems, they still lack components for dynamicity and privacy protection, which leads to not have desire levels of security and to the patient not to have a full and easy control of his privacy. Within this master thesis, after a deep research and review of the stat of art, was published a novel dynamic access control model, Socio-Technical Risk-Adaptable Access Control modEl (SoTRAACE), which can model the inherent differences and security requirements that are present in this thesis. To do this, SoTRAACE aggregates attributes from various domains to help performing a risk assessment at the moment of the request. The assessment of the risk factors identified in this work is based in a Delphi Study. A set of security experts from various domains were selected, to classify the impact in the risk assessment of each attribute that SoTRAACE aggregates. SoTRAACE was integrated in an architecture with requirements well-founded, and based in the best recommendations and standards (OWASP, NIST 800-53, NIST 800-57), as well based in deep review of the state-of-art. The architecture is further targeted with the essential security analysis and the threat model. As proof of concept, the proposed access control model was implemented within the user-centric architecture, with two mobile prototypes for several types of accesses by patients and healthcare professionals, as well the web servers that handles the access requests, authentication and identity management. The proof of concept shows that the model works as expected, with transparency, assuring privacy and data control to the user without impact for user experience and interaction. It is clear that the model can be extended to other industry domains, and new levels of risks or attributes can be added because it is modular. The architecture also works as expected, assuring secure authentication with multifactor, and secure data share/access based in SoTRAACE decisions. The communication channel that SoTRAACE uses was also protected with a digital certificate. At last, the architecture was tested within different Android versions, tested with static and dynamic analysis and with tests with security tools. Future work includes the integration of health data standards and evaluating the proposed system by collecting users’ opinion after releasing the system to real world.Hoje em dia vivemos em um paradigma móvel de acesso em qualquer lugar/hora, sendo que os dispositivos móveis são a tecnologia mais presente no dia a dia da sociedade. Devido à sua portabilidade, disponibilidade, fácil manuseamento, poder de comunicação, acesso e partilha de informação referentes a várias áreas e domínios das nossas vidas, a aceitação e integração destes dispositivos é cada vez maior. No entanto, devido ao seu potencial e aumento do número de utilizadores, os dispositivos móveis são cada vez mais alvos de ataques, e tal como outras tecnologias, aplicações móveis continuam a ser vulneráveis. Sistemas de informação de saúde são compostos por ferramentas e softwares que permitem recolher, administrar, analisar e processar informação médica (tais como documentos de saúde eletrónicos). Portanto, tais sistemas podem potencializar a performance e a manutenção dos serviços de saúde, promovendo assim a disponibilidade, acessibilidade e a partilha de dados vitais referentes ao registro médico geral dos pacientes, entre serviços e instituições que estão geograficamente fragmentadas. O rápido acesso a informações médicas apresenta uma grande importância para o setor da saúde, dado que acelera os processos de trabalho, resultando assim numa melhor eficiência na utilização do tempo e recursos. Consequentemente haverá uma melhor qualidade de tratamento. Porém os sistemas de informação de saúde armazenam e manuseiam dados bastantes sensíveis, o que levanta sérias preocupações referentes à privacidade e segurança do paciente. Assim se explica o aumento de incidentes maliciosos dentro do domínio da saúde. Os dados de saúde são altamente sensíveis e são sujeitos a severas leis e restrições regulamentares, que pretendem assegurar a proteção dos direitos e privacidade dos pacientes, salvaguardando os seus dados de saúde. Juntamente com estas legislações, requerimentos de segurança devem ser analisados e medidas implementadas. Dentro dos requerimentos necessários para aceder aos dados de saúde, uma autenticação segura, gestão de identidade e controlos de acesso são essenciais para fornecer meios adequados para a proteção de dados contra acessos não autorizados. No entanto, além do uso de modelos simples de autenticação, os modelos tradicionais de controlo de acesso são normalmente baseados em políticas de acesso e cargos pré-definidos, e são inflexíveis. Isto resulta em decisões de controlo de acesso uniformes para diferentes pessoas, tipos de dispositivo, ambientes e condições situacionais, empresas, localizações e diferentes alturas no tempo. Apesar dos modelos existentes permitirem assegurar algumas necessidades dos sistemas de saúde, ainda há escassez de componentes para accesso dinâmico e proteção de privacidade , o que resultam em níveis de segurança não satisfatórios e em o paciente não ter controlo directo e total sobre a sua privacidade e documentos de saúde. Dentro desta tese de mestrado, depois da investigação e revisão intensiva do estado da arte, foi publicado um modelo inovador de controlo de acesso, chamado SoTRAACE, que molda as diferenças de acesso inerentes e requerimentos de segurança presentes nesta tese. Para isto, o SoTRAACE agrega atributos de vários ambientes e domínios que ajudam a executar uma avaliação de riscos, no momento em que os dados são requisitados. A avaliação dos fatores de risco identificados neste trabalho são baseados num estudo de Delphi. Um conjunto de peritos de segurança de vários domínios industriais foram selecionados, para classificar o impacto de cada atributo que o SoTRAACE agrega. O SoTRAACE foi integrado numa arquitectura para acesso a dados médicos, com requerimentos bem fundados, baseados nas melhores normas e recomendações (OWASP, NIST 800-53, NIST 800-57), e em revisões intensivas do estado da arte. Esta arquitectura é posteriormente alvo de uma análise de segurança e modelos de ataque. Como prova deste conceito, o modelo de controlo de acesso proposto é implementado juntamente com uma arquitetura focada no utilizador, com dois protótipos para aplicações móveis, que providênciam vários tipos de acesso de pacientes e profissionais de saúde. A arquitetura é constituída também por servidores web que tratam da gestão de dados, controlo de acesso e autenticação e gestão de identidade. O resultado final mostra que o modelo funciona como esperado, com transparência, assegurando a privacidade e o controlo de dados para o utilizador, sem ter impacto na sua interação e experiência. Consequentemente este modelo pode-se extender para outros setores industriais, e novos níveis de risco ou atributos podem ser adicionados a este mesmo, por ser modular. A arquitetura também funciona como esperado, assegurando uma autenticação segura com multi-fator, acesso e partilha de dados segura baseado em decisões do SoTRAACE. O canal de comunicação que o SoTRAACE usa foi também protegido com um certificado digital. A arquitectura foi testada em diferentes versões de Android, e foi alvo de análise estática, dinâmica e testes com ferramentas de segurança. Para trabalho futuro está planeado a integração de normas de dados de saúde e a avaliação do sistema proposto, através da recolha de opiniões de utilizadores no mundo real