6 research outputs found

    Decision Support Elements and Enabling Techniques to Achieve a Cyber Defence Situational Awareness Capability

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    [ES] La presente tesis doctoral realiza un análisis en detalle de los elementos de decisión necesarios para mejorar la comprensión de la situación en ciberdefensa con especial énfasis en la percepción y comprensión del analista de un centro de operaciones de ciberseguridad (SOC). Se proponen dos arquitecturas diferentes basadas en el análisis forense de flujos de datos (NF3). La primera arquitectura emplea técnicas de Ensemble Machine Learning mientras que la segunda es una variante de Machine Learning de mayor complejidad algorítmica (lambda-NF3) que ofrece un marco de defensa de mayor robustez frente a ataques adversarios. Ambas propuestas buscan automatizar de forma efectiva la detección de malware y su posterior gestión de incidentes mostrando unos resultados satisfactorios en aproximar lo que se ha denominado un SOC de próxima generación y de computación cognitiva (NGC2SOC). La supervisión y monitorización de eventos para la protección de las redes informáticas de una organización debe ir acompañada de técnicas de visualización. En este caso, la tesis aborda la generación de representaciones tridimensionales basadas en métricas orientadas a la misión y procedimientos que usan un sistema experto basado en lógica difusa. Precisamente, el estado del arte muestra serias deficiencias a la hora de implementar soluciones de ciberdefensa que reflejen la relevancia de la misión, los recursos y cometidos de una organización para una decisión mejor informada. El trabajo de investigación proporciona finalmente dos áreas claves para mejorar la toma de decisiones en ciberdefensa: un marco sólido y completo de verificación y validación para evaluar parámetros de soluciones y la elaboración de un conjunto de datos sintéticos que referencian unívocamente las fases de un ciberataque con los estándares Cyber Kill Chain y MITRE ATT & CK.[CA] La present tesi doctoral realitza una anàlisi detalladament dels elements de decisió necessaris per a millorar la comprensió de la situació en ciberdefensa amb especial èmfasi en la percepció i comprensió de l'analista d'un centre d'operacions de ciberseguretat (SOC). Es proposen dues arquitectures diferents basades en l'anàlisi forense de fluxos de dades (NF3). La primera arquitectura empra tècniques de Ensemble Machine Learning mentre que la segona és una variant de Machine Learning de major complexitat algorítmica (lambda-NF3) que ofereix un marc de defensa de major robustesa enfront d'atacs adversaris. Totes dues propostes busquen automatitzar de manera efectiva la detecció de malware i la seua posterior gestió d'incidents mostrant uns resultats satisfactoris a aproximar el que s'ha denominat un SOC de pròxima generació i de computació cognitiva (NGC2SOC). La supervisió i monitoratge d'esdeveniments per a la protecció de les xarxes informàtiques d'una organització ha d'anar acompanyada de tècniques de visualització. En aquest cas, la tesi aborda la generació de representacions tridimensionals basades en mètriques orientades a la missió i procediments que usen un sistema expert basat en lògica difusa. Precisament, l'estat de l'art mostra serioses deficiències a l'hora d'implementar solucions de ciberdefensa que reflectisquen la rellevància de la missió, els recursos i comeses d'una organització per a una decisió més ben informada. El treball de recerca proporciona finalment dues àrees claus per a millorar la presa de decisions en ciberdefensa: un marc sòlid i complet de verificació i validació per a avaluar paràmetres de solucions i l'elaboració d'un conjunt de dades sintètiques que referencien unívocament les fases d'un ciberatac amb els estàndards Cyber Kill Chain i MITRE ATT & CK.[EN] This doctoral thesis performs a detailed analysis of the decision elements necessary to improve the cyber defence situation awareness with a special emphasis on the perception and understanding of the analyst of a cybersecurity operations center (SOC). Two different architectures based on the network flow forensics of data streams (NF3) are proposed. The first architecture uses Ensemble Machine Learning techniques while the second is a variant of Machine Learning with greater algorithmic complexity (lambda-NF3) that offers a more robust defense framework against adversarial attacks. Both proposals seek to effectively automate the detection of malware and its subsequent incident management, showing satisfactory results in approximating what has been called a next generation cognitive computing SOC (NGC2SOC). The supervision and monitoring of events for the protection of an organisation's computer networks must be accompanied by visualisation techniques. In this case, the thesis addresses the representation of three-dimensional pictures based on mission oriented metrics and procedures that use an expert system based on fuzzy logic. Precisely, the state-of-the-art evidences serious deficiencies when it comes to implementing cyber defence solutions that consider the relevance of the mission, resources and tasks of an organisation for a better-informed decision. The research work finally provides two key areas to improve decision-making in cyber defence: a solid and complete verification and validation framework to evaluate solution parameters and the development of a synthetic dataset that univocally references the phases of a cyber-attack with the Cyber Kill Chain and MITRE ATT & CK standards.Llopis Sánchez, S. (2023). Decision Support Elements and Enabling Techniques to Achieve a Cyber Defence Situational Awareness Capability [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/19424

    Advanced Threat Intelligence: Interpretation of Anomalous Behavior in Ubiquitous Kernel Processes

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    Targeted attacks on digital infrastructures are a rising threat against the confidentiality, integrity, and availability of both IT systems and sensitive data. With the emergence of advanced persistent threats (APTs), identifying and understanding such attacks has become an increasingly difficult task. Current signature-based systems are heavily reliant on fixed patterns that struggle with unknown or evasive applications, while behavior-based solutions usually leave most of the interpretative work to a human analyst. This thesis presents a multi-stage system able to detect and classify anomalous behavior within a user session by observing and analyzing ubiquitous kernel processes. Application candidates suitable for monitoring are initially selected through an adapted sentiment mining process using a score based on the log likelihood ratio (LLR). For transparent anomaly detection within a corpus of associated events, the author utilizes star structures, a bipartite representation designed to approximate the edit distance between graphs. Templates describing nominal behavior are generated automatically and are used for the computation of both an anomaly score and a report containing all deviating events. The extracted anomalies are classified using the Random Forest (RF) and Support Vector Machine (SVM) algorithms. Ultimately, the newly labeled patterns are mapped to a dedicated APT attacker–defender model that considers objectives, actions, actors, as well as assets, thereby bridging the gap between attack indicators and detailed threat semantics. This enables both risk assessment and decision support for mitigating targeted attacks. Results show that the prototype system is capable of identifying 99.8% of all star structure anomalies as benign or malicious. In multi-class scenarios that seek to associate each anomaly with a distinct attack pattern belonging to a particular APT stage we achieve a solid accuracy of 95.7%. Furthermore, we demonstrate that 88.3% of observed attacks could be identified by analyzing and classifying a single ubiquitous Windows process for a mere 10 seconds, thereby eliminating the necessity to monitor each and every (unknown) application running on a system. With its semantic take on threat detection and classification, the proposed system offers a formal as well as technical solution to an information security challenge of great significance.The financial support by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs, and the National Foundation for Research, Technology and Development is gratefully acknowledged

    Harnessing Human Potential for Security Analytics

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    Humans are often considered the weakest link in cybersecurity. As a result, their potential has been continuously neglected. However, in recent years there is a contrasting development recognizing that humans can benefit the area of security analytics, especially in the case of security incidents that leave no technical traces. Therefore, the demand becomes apparent to see humans not only as a problem but also as part of the solution. In line with this shift in the perception of humans, the present dissertation pursues the research vision to evolve from a human-as-a-problem to a human-as-a-solution view in cybersecurity. A step in this direction is taken by exploring the research question of how humans can be integrated into security analytics to contribute to the improvement of the overall security posture. In addition to laying foundations in the field of security analytics, this question is approached from two directions. On the one hand, an approach in the context of the human-as-a-security-sensor paradigm is developed which harnesses the potential of security novices to detect security incidents while maintaining high data quality of human-provided information. On the other hand, contributions are made to better leverage the potential of security experts within a SOC. Besides elaborating the current state in research, a tool for determining the target state of a SOC in the form of a maturity model is developed. Based on this, the integration of security experts was improved by the innovative application of digital twins within SOCs. Accordingly, a framework is created that improves manual security analyses by simulating attacks within a digital twin. Furthermore, a cyber range was created, which offers a realistic training environment for security experts based on this digital twin

    Towards a standardised attack graph visual syntax

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    More research needs to focus on developing effective methods of aiding the understanding and perception of cyber-attacks. Attack modelling techniques (AMTs) - such as attack graphs, attack trees and fault trees, are popular methods of mathematically and visually representing the sequence of events that lead to a successful cyber-attack. Although useful in aiding cyber-attack perception, there is little empirical or comparative research which evaluates the effectiveness of these methods. Furthermore, there is no standardised attack graph visual syntax configuration, currently more than seventy-five self-nominated attack graph and twenty attack tree configurations have been described in the literature - each of which presents attributes such as preconditions and exploits in a different way. This research analyses methods of presenting cyber-attacks and reveals that attack graphs and attack trees are the dominant methods. The research proposes an attack graph visual syntax which is designed using evidence based principles. The proposed attack graph is compared with the fault tree - which is a standard method of representing events such as cyber-attacks. This comparison shows that the proposed attack graph visual syntax is more effective than the fault tree method at aiding cyber-attack perception and that the attack graph can be an effective tool for aiding cyber-attack perception - particularly in educational contexts. Although the proposed attack graph visual syntax is shown to be cognitively effective, this is no indication of practitioner acceptance. The research proceeds to identify a preferred attack graph visual syntax from a range of visual syntaxes - one of which is the proposed attack graph visual syntax. The method used to perform the comparison is conjoint analysis which is innovative for this field. The results of the second study reveal that the proposed attack graph visual syntax is one of the preferred configurations. This attack graph has the following attributes. The flow of events is represented top-down, preconditions are represented as rectangles, and exploits are represented as ellipses. The key contribution of this research is the development of an attack graph visual syntax which is effective in aiding the understanding of cyber-attacks particularly in educational contexts. The proposed method is a significant step towards standardising the attack graph visual syntax

    Translation Alignment Applied to Historical Languages: methods, evaluation, applications, and visualization

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    Translation alignment is an essential task in Digital Humanities and Natural Language Processing, and it aims to link words/phrases in the source text with their translation equivalents in the translation. In addition to its importance in teaching and learning historical languages, translation alignment builds bridges between ancient and modern languages through which various linguistics annotations can be transferred. This thesis focuses on word-level translation alignment applied to historical languages in general and Ancient Greek and Latin in particular. As the title indicates, the thesis addresses four interdisciplinary aspects of translation alignment. The starting point was developing Ugarit, an interactive annotation tool to perform manual alignment aiming to gather training data to train an automatic alignment model. This effort resulted in more than 190k accurate translation pairs that I used for supervised training later. Ugarit has been used by many researchers and scholars also in the classroom at several institutions for teaching and learning ancient languages, which resulted in a large, diverse crowd-sourced aligned parallel corpus allowing us to conduct experiments and qualitative analysis to detect recurring patterns in annotators’ alignment practice and the generated translation pairs. Further, I employed the recent advances in NLP and language modeling to develop an automatic alignment model for historical low-resourced languages, experimenting with various training objectives and proposing a training strategy for historical languages that combines supervised and unsupervised training with mono- and multilingual texts. Then, I integrated this alignment model into other development workflows to project cross-lingual annotations and induce bilingual dictionaries from parallel corpora. Evaluation is essential to assess the quality of any model. To ensure employing the best practice, I reviewed the current evaluation procedure, defined its limitations, and proposed two new evaluation metrics. Moreover, I introduced a visual analytics framework to explore and inspect alignment gold standard datasets and support quantitative and qualitative evaluation of translation alignment models. Besides, I designed and implemented visual analytics tools and reading environments for parallel texts and proposed various visualization approaches to support different alignment-related tasks employing the latest advances in information visualization and best practice. Overall, this thesis presents a comprehensive study that includes manual and automatic alignment techniques, evaluation methods and visual analytics tools that aim to advance the field of translation alignment for historical languages

    Supporting users in password authentication with persuasive design

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    Activities like text-editing, watching movies, or managing personal finances are all accomplished with web-based solutions nowadays. The providers need to ensure security and privacy of user data. To that end, passwords are still the most common authentication method on the web. They are inexpensive and easy to implement. Users are largely accustomed to this kind of authentication but passwords represent a considerable nuisance, because they are tedious to create, remember, and maintain. In many cases, usability issues turn into security problems, because users try to work around the challenges and create easily predictable credentials. Often, they reuse their passwords for many purposes, which aggravates the risk of identity theft. There have been numerous attempts to remove the root of the problem and replace passwords, e.g., through biometrics. However, no other authentication strategy can fully replace them, so passwords will probably stay a go-to authentication method for the foreseeable future. Researchers and practitioners have thus aimed to improve users' situation in various ways. There are two main lines of research on helping users create both usable and secure passwords. On the one hand, password policies have a notable impact on password practices, because they enforce certain characteristics. However, enforcement reduces users' autonomy and often causes frustration if the requirements are poorly communicated or overly complex. On the other hand, user-centered designs have been proposed: Assistance and persuasion are typically more user-friendly but their influence is often limited. In this thesis, we explore potential reasons for the inefficacy of certain persuasion strategies. From the gained knowledge, we derive novel persuasive design elements to support users in password authentication. The exploration of contextual factors in password practices is based on four projects that reveal both psychological aspects and real-world constraints. Here, we investigate how mental models of password strength and password managers can provide important pointers towards the design of persuasive interventions. Moreover, the associations between personality traits and password practices are evaluated in three user studies. A meticulous audit of real-world password policies shows the constraints for selection and reuse practices. Based on the review of context factors, we then extend the design space of persuasive password support with three projects. We first depict the explicit and implicit user needs in password support. Second, we craft and evaluate a choice architecture that illustrates how a phenomenon from marketing psychology can provide new insights into the design of nudging strategies. Third, we tried to empower users to create memorable passwords with emojis. The results show the challenges and potentials of emoji-passwords on different platforms. Finally, the thesis presents a framework for the persuasive design of password support. It aims to structure the required activities during the entire process. This enables researchers and practitioners to craft novel systems that go beyond traditional paradigms, which is illustrated by a design exercise.Heutzutage ist es möglich, mit web-basierten Lösungen Texte zu editieren, Filme anzusehen, oder seine persönlichen Finanzen zu verwalten. Die Anbieter müssen hierbei Sicherheit und Vertraulichkeit von Nutzerdaten sicherstellen. Dazu sind Passwörter weiterhin die geläufigste Authentifizierungsmethode im Internet. Sie sind kostengünstig und einfach zu implementieren. NutzerInnen sind bereits im Umgang mit diesem Verfahren vertraut jedoch stellen Passwörter ein beträchtliches Ärgernis dar, weil sie mühsam zu erstellen, einzuprägen, und verwalten sind. Oft werden Usabilityfragen zu Sicherheitsproblemen, weil NutzerInnen Herausforderungen umschiffen und sich einfach zu erratende Zugangsdaten ausdenken. Daneben verwenden sie Passwörter für viele Zwecke wieder, was das Risiko eines Identitätsdiebstals weiter erhöht. Es gibt zahlreiche Versuche die Wurzel des Problems zu beseitigen und Passwörter zu ersetzen, z.B. mit Biometrie. Jedoch kann bisher kein anderes Verfahren sie vollkommen ersetzen, so dass Passwörter wohl für absehbare Zeit die Hauptauthentifizierungsmethode bleiben werden. ExpertInnen aus Forschung und Industrie haben sich deshalb zum Ziel gefasst, die Situation der NutzerInnen auf verschiedene Wege zu verbessern. Es existieren zwei Forschungsstränge darüber wie man NutzerInnen bei der Erstellung von sicheren und benutzbaren Passwörtern helfen kann. Auf der einen Seite haben Regeln bei der Passworterstellung deutliche Auswirkungen auf Passwortpraktiken, weil sie bestimmte Charakteristiken durchsetzen. Jedoch reduziert diese Durchsetzung die Autonomie der NutzerInnen und verursacht Frustration, wenn die Anforderungen schlecht kommuniziert oder übermäßig komplex sind. Auf der anderen Seite stehen nutzerzentrierte Designs: Hilfestellung und Überzeugungsarbeit sind typischerweise nutzerfreundlicher wobei ihr Einfluss begrenzt ist. In dieser Arbeit erkunden wir die potenziellen Gründe für die Ineffektivität bestimmter Überzeugungsstrategien. Von dem hierbei gewonnenen Wissen leiten wir neue persuasive Designelemente für Hilfestellung bei der Passwortauthentifizierung ab. Die Exploration von Kontextfaktoren im Umgang mit Passwörtern basiert auf vier Projekten, die sowohl psychologische Aspekte als auch Einschränkungen in der Praxis aufdecken. Hierbei untersuchen wir inwiefern Mental Modelle von Passwortstärke und -managern wichtige Hinweise auf das Design von persuasiven Interventionen liefern. Darüber hinaus werden die Zusammenhänge zwischen Persönlichkeitsmerkmalen und Passwortpraktiken in drei Nutzerstudien untersucht. Eine gründliche Überprüfung von Passwortregeln in der Praxis zeigt die Einschränkungen für Passwortselektion und -wiederverwendung. Basierend auf der Durchleuchtung der Kontextfaktoren erweitern wir hierauf den Design-Raum von persuasiver Passworthilfestellung mit drei Projekten. Zuerst schildern wir die expliziten und impliziten Bedürfnisse in punkto Hilfestellung. Daraufhin erstellen und evaluieren wir eine Entscheidungsarchitektur, welche veranschaulicht wie ein Phänomen aus der Marketingpsychologie neue Einsichten in das Design von Nudging-Strategien liefern kann. Im Schlussgang versuchen wir NutzerInnen dabei zu stärken, gut merkbare Passwörter mit Hilfe von Emojis zu erstellen. Die Ergebnisse zeigen die Herausforderungen und Potenziale von Emoji-Passwörtern auf verschiedenen Plattformen. Zuletzt präsentiert diese Arbeit ein Rahmenkonzept für das persuasive Design von Passworthilfestellungen. Es soll die benötigten Aktivitäten während des gesamten Prozesses strukturieren. Dies erlaubt ExpertInnen neuartige Systeme zu entwickeln, die über traditionelle Ansätze hinausgehen, was durch eine Designstudie veranschaulicht wird
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