Tracking advanced persistent threats in critical infrastructures through opinion dynamics

Advanced persistent threats pose a serious issue for modern industrial environments, due to their targeted and complex attack vectors that are difficult to detect. This is especially severe in critical infrastructures that are accelerating the integration of IT technologies. It is then essential to further develop effective monitoring and response systems that ensure the continuity of business to face the arising set of cyber-security threats. In this paper, we study the practical applicability of a novel technique based on opinion dynamics, that permits to trace the attack throughout all its stages along the network by correlating different anomalies measured over time, thereby taking the persistence of threats and the criticality of resources into consideration. The resulting information is of essential importance to monitor the overall health of the control system and cor- respondingly deploy accurate response procedures. Advanced Persistent Threat Detection Traceability Opinion Dynamics.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Intensional Cyberforensics

This work focuses on the application of intensional logic to cyberforensic analysis and its benefits and difficulties are compared with the finite-state-automata approach. This work extends the use of the intensional programming paradigm to the modeling and implementation of a cyberforensics investigation process with backtracing of event reconstruction, in which evidence is modeled by multidimensional hierarchical contexts, and proofs or disproofs of claims are undertaken in an eductive manner of evaluation. This approach is a practical, context-aware improvement over the finite state automata (FSA) approach we have seen in previous work. As a base implementation language model, we use in this approach a new dialect of the Lucid programming language, called Forensic Lucid, and we focus on defining hierarchical contexts based on intensional logic for the distributed evaluation of cyberforensic expressions. We also augment the work with credibility factors surrounding digital evidence and witness accounts, which have not been previously modeled. The Forensic Lucid programming language, used for this intensional cyberforensic analysis, formally presented through its syntax and operational semantics. In large part, the language is based on its predecessor and codecessor Lucid dialects, such as GIPL, Indexical Lucid, Lucx, Objective Lucid, and JOOIP bound by the underlying intensional programming paradigm.Comment: 412 pages, 94 figures, 18 tables, 19 algorithms and listings; PhD thesis; v2 corrects some typos and refs; also available on Spectrum at http://spectrum.library.concordia.ca/977460

Automating Security Risk and Requirements Management for Cyber-Physical Systems

Cyber-physische Systeme ermöglichen zahlreiche moderne Anwendungsfälle und Geschäftsmodelle wie vernetzte Fahrzeuge, das intelligente Stromnetz (Smart Grid) oder das industrielle Internet der Dinge. Ihre Schlüsselmerkmale Komplexität, Heterogenität und Langlebigkeit machen den langfristigen Schutz dieser Systeme zu einer anspruchsvollen, aber unverzichtbaren Aufgabe. In der physischen Welt stellen die Gesetze der Physik einen festen Rahmen für Risiken und deren Behandlung dar. Im Cyberspace gibt es dagegen keine vergleichbare Konstante, die der Erosion von Sicherheitsmerkmalen entgegenwirkt. Hierdurch können sich bestehende Sicherheitsrisiken laufend ändern und neue entstehen. Um Schäden durch böswillige Handlungen zu verhindern, ist es notwendig, hohe und unbekannte Risiken frühzeitig zu erkennen und ihnen angemessen zu begegnen. Die Berücksichtigung der zahlreichen dynamischen sicherheitsrelevanten Faktoren erfordert einen neuen Automatisierungsgrad im Management von Sicherheitsrisiken und -anforderungen, der über den aktuellen Stand der Wissenschaft und Technik hinausgeht. Nur so kann langfristig ein angemessenes, umfassendes und konsistentes Sicherheitsniveau erreicht werden. Diese Arbeit adressiert den dringenden Bedarf an einer Automatisierungsmethodik bei der Analyse von Sicherheitsrisiken sowie der Erzeugung und dem Management von Sicherheitsanforderungen für Cyber-physische Systeme. Das dazu vorgestellte Rahmenwerk umfasst drei Komponenten: (1) eine modelbasierte Methodik zur Ermittlung und Bewertung von Sicherheitsrisiken; (2) Methoden zur Vereinheitlichung, Ableitung und Verwaltung von Sicherheitsanforderungen sowie (3) eine Reihe von Werkzeugen und Verfahren zur Erkennung und Reaktion auf sicherheitsrelevante Situationen. Der Schutzbedarf und die angemessene Stringenz werden durch die Sicherheitsrisikobewertung mit Hilfe von Graphen und einer sicherheitsspezifischen Modellierung ermittelt und bewertet. Basierend auf dem Modell und den bewerteten Risiken werden anschließend fundierte Sicherheitsanforderungen zum Schutz des Gesamtsystems und seiner Funktionalität systematisch abgeleitet und in einer einheitlichen, maschinenlesbaren Struktur formuliert. Diese maschinenlesbare Struktur ermöglicht es, Sicherheitsanforderungen automatisiert entlang der Lieferkette zu propagieren. Ebenso ermöglicht sie den effizienten Abgleich der vorhandenen Fähigkeiten mit externen Sicherheitsanforderungen aus Vorschriften, Prozessen und von Geschäftspartnern. Trotz aller getroffenen Maßnahmen verbleibt immer ein gewisses Restrisiko einer Kompromittierung, worauf angemessen reagiert werden muss. Dieses Restrisiko wird durch Werkzeuge und Prozesse adressiert, die sowohl die lokale und als auch die großräumige Erkennung, Klassifizierung und Korrelation von Vorfällen verbessern. Die Integration der Erkenntnisse aus solchen Vorfällen in das Modell führt häufig zu aktualisierten Bewertungen, neuen Anforderungen und verbessert weitere Analysen. Abschließend wird das vorgestellte Rahmenwerk anhand eines aktuellen Anwendungsfalls aus dem Automobilbereich demonstriert.Cyber-Physical Systems enable various modern use cases and business models such as connected vehicles, the Smart (power) Grid, or the Industrial Internet of Things. Their key characteristics, complexity, heterogeneity, and longevity make the long-term protection of these systems a demanding but indispensable task. In the physical world, the laws of physics provide a constant scope for risks and their treatment. In cyberspace, on the other hand, there is no such constant to counteract the erosion of security features. As a result, existing security risks can constantly change and new ones can arise. To prevent damage caused by malicious acts, it is necessary to identify high and unknown risks early and counter them appropriately. Considering the numerous dynamic security-relevant factors requires a new level of automation in the management of security risks and requirements, which goes beyond the current state of the art. Only in this way can an appropriate, comprehensive, and consistent level of security be achieved in the long term. This work addresses the pressing lack of an automation methodology for the security-risk assessment as well as the generation and management of security requirements for Cyber-Physical Systems. The presented framework accordingly comprises three components: (1) a model-based security risk assessment methodology, (2) methods to unify, deduce and manage security requirements, and (3) a set of tools and procedures to detect and respond to security-relevant situations. The need for protection and the appropriate rigor are determined and evaluated by the security risk assessment using graphs and a security-specific modeling. Based on the model and the assessed risks, well-founded security requirements for protecting the overall system and its functionality are systematically derived and formulated in a uniform, machine-readable structure. This machine-readable structure makes it possible to propagate security requirements automatically along the supply chain. Furthermore, they enable the efficient reconciliation of present capabilities with external security requirements from regulations, processes, and business partners. Despite all measures taken, there is always a slight risk of compromise, which requires an appropriate response. This residual risk is addressed by tools and processes that improve the local and large-scale detection, classification, and correlation of incidents. Integrating the findings from such incidents into the model often leads to updated assessments, new requirements, and improves further analyses. Finally, the presented framework is demonstrated by a recent application example from the automotive domain

Master of Science in Computer Science

thesisIn today's IP networks, any host can send packets to any other host irrespective of whether the recipient is interested in communicating with the sender or not. The downside of this openness is that every host is vulnerable to an attack by any other host. We ob- serve that this unrestricted network access (network ambient authority) from compromised systems is also a main reason for data exfiltration attacks within corporate networks. We address this issue using the network version of capability based access control. We bring the idea of capabilities and capability-based access control to the domain of networking. CeNet provides policy driven, fine-grained network level access control enforced in the core of the network (and not at the end-hosts) thereby removing network ambient authority. Thus CeNet is able to limit the scope of spread of an attack from a compromised host to other hosts in the network. We built a capability-enabled SDN network where communication privileges of an endpoint are limited according to its function in the network. Network capabilities can be passed between hosts, thereby allowing a delegation-oriented security policy to be realized. We believe that this base functionality can pave the way for the realization of sophisticated security policies within an enterprise network. Further we built a policy manager that is able to realize Role-Based Access Control (RBAC) policy based network access control using capability operations. We also look at some of the results of formal analysis of capability propagation models in the context of networks

Reasoning about Cyber Threat Actors

abstract: Reasoning about the activities of cyber threat actors is critical to defend against cyber attacks. However, this task is difficult for a variety of reasons. In simple terms, it is difficult to determine who the attacker is, what the desired goals are of the attacker, and how they will carry out their attacks. These three questions essentially entail understanding the attacker’s use of deception, the capabilities available, and the intent of launching the attack. These three issues are highly inter-related. If an adversary can hide their intent, they can better deceive a defender. If an adversary’s capabilities are not well understood, then determining what their goals are becomes difficult as the defender is uncertain if they have the necessary tools to accomplish them. However, the understanding of these aspects are also mutually supportive. If we have a clear picture of capabilities, intent can better be deciphered. If we understand intent and capabilities, a defender may be able to see through deception schemes. In this dissertation, I present three pieces of work to tackle these questions to obtain a better understanding of cyber threats. First, we introduce a new reasoning framework to address deception. We evaluate the framework by building a dataset from DEFCON capture-the-flag exercise to identify the person or group responsible for a cyber attack. We demonstrate that the framework not only handles cases of deception but also provides transparent decision making in identifying the threat actor. The second task uses a cognitive learning model to determine the intent – goals of the threat actor on the target system. The third task looks at understanding the capabilities of threat actors to target systems by identifying at-risk systems from hacker discussions on darkweb websites. To achieve this task we gather discussions from more than 300 darkweb websites relating to malicious hacking.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Computing Competencies for Undergraduate Data Science Curricula: ACM Data Science Task Force

At the August 2017 ACM Education Council meeting, a task force was formed to explore a process to add to the broad, interdisciplinary conversation on data science, with an articulation of the role of computing discipline-specific contributions to this emerging field. Specifically, the task force would seek to define what the computing/computational contributions are to this new field, and provide guidance on computing-specific competencies in data science for departments offering such programs of study at the undergraduate level. There are many stakeholders in the discussion of data science – these include colleges and universities that (hope to) offer data science programs, employers who hope to hire a workforce with knowledge and experience in data science, as well as individuals and professional societies representing the fields of computing, statistics, machine learning, computational biology, computational social sciences, digital humanities, and others. There is a shared desire to form a broad interdisciplinary definition of data science and to develop curriculum guidance for degree programs in data science. This volume builds upon the important work of other groups who have published guidelines for data science education. There is a need to acknowledge the definition and description of the individual contributions to this interdisciplinary field. For instance, those interested in the business context for these concepts generally use the term “analytics”; in some cases, the abbreviation DSA appears, meaning Data Science and Analytics. This volume is the third draft articulation of computing-focused competencies for data science. It recognizes the inherent interdisciplinarity of data science and situates computing-specific competencies within the broader interdisciplinary space