1,908 research outputs found
Supporting Cyber-Physical Systems with Wireless Sensor Networks: An Outlook of Software and Services
Sensing, communication, computation and control technologies are the essential building blocks of a cyber-physical system (CPS). Wireless sensor networks (WSNs) are a way to support CPS as they provide fine-grained spatial-temporal sensing, communication and computation at a low premium of cost and power. In this article, we explore the fundamental concepts guiding the design and implementation of WSNs. We report the latest developments in WSN software and services for meeting existing requirements and newer demands; particularly in the areas of: operating system, simulator and emulator, programming abstraction, virtualization, IP-based communication and security, time and location, and network monitoring and management. We also reflect on the ongoing
efforts in providing dependable assurances for WSN-driven CPS. Finally, we report on its applicability with a case-study on smart buildings
Automating Security Risk and Requirements Management for Cyber-Physical Systems
Cyber-physische Systeme ermöglichen zahlreiche moderne AnwendungsfÀlle und GeschÀftsmodelle wie vernetzte Fahrzeuge, das intelligente Stromnetz (Smart Grid) oder das industrielle Internet der Dinge.
Ihre SchlĂŒsselmerkmale KomplexitĂ€t, HeterogenitĂ€t 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 fĂŒr 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 Àndern und neue entstehen.
Um SchĂ€den durch böswillige Handlungen zu verhindern, ist es notwendig, hohe und unbekannte Risiken frĂŒhzeitig zu erkennen und ihnen angemessen zu begegnen.
Die BerĂŒcksichtigung der zahlreichen dynamischen sicherheitsrelevanten Faktoren erfordert einen neuen Automatisierungsgrad im Management von Sicherheitsrisiken und -anforderungen, der ĂŒber 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 fĂŒr 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 anschlieĂend fundierte Sicherheitsanforderungen zum Schutz des Gesamtsystems und seiner FunktionalitĂ€t 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 FÀhigkeiten mit externen Sicherheitsanforderungen aus Vorschriften, Prozessen und von GeschÀftspartnern.
Trotz aller getroffenen MaĂnahmen 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 groĂrĂ€umige Erkennung, Klassifizierung und Korrelation von VorfĂ€llen verbessern. Die Integration der Erkenntnisse aus solchen VorfĂ€llen in das Modell fĂŒhrt hĂ€ufig zu aktualisierten Bewertungen, neuen Anforderungen und verbessert weitere Analysen.
AbschlieĂend 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
Securing Real-Time Internet-of-Things
Modern embedded and cyber-physical systems are ubiquitous. A large number of
critical cyber-physical systems have real-time requirements (e.g., avionics,
automobiles, power grids, manufacturing systems, industrial control systems,
etc.). Recent developments and new functionality requires real-time embedded
devices to be connected to the Internet. This gives rise to the real-time
Internet-of-things (RT-IoT) that promises a better user experience through
stronger connectivity and efficient use of next-generation embedded devices.
However RT- IoT are also increasingly becoming targets for cyber-attacks which
is exacerbated by this increased connectivity. This paper gives an introduction
to RT-IoT systems, an outlook of current approaches and possible research
challenges towards secure RT- IoT frameworks
Security and Privacy Issues of Big Data
This chapter revises the most important aspects in how computing
infrastructures should be configured and intelligently managed to fulfill the
most notably security aspects required by Big Data applications. One of them is
privacy. It is a pertinent aspect to be addressed because users share more and
more personal data and content through their devices and computers to social
networks and public clouds. So, a secure framework to social networks is a very
hot topic research. This last topic is addressed in one of the two sections of
the current chapter with case studies. In addition, the traditional mechanisms
to support security such as firewalls and demilitarized zones are not suitable
to be applied in computing systems to support Big Data. SDN is an emergent
management solution that could become a convenient mechanism to implement
security in Big Data systems, as we show through a second case study at the end
of the chapter. This also discusses current relevant work and identifies open
issues.Comment: In book Handbook of Research on Trends and Future Directions in Big
Data and Web Intelligence, IGI Global, 201
Beyond Good and Evil: Formalizing the Security Guarantees of Compartmentalizing Compilation
Compartmentalization is good security-engineering practice. By breaking a
large software system into mutually distrustful components that run with
minimal privileges, restricting their interactions to conform to well-defined
interfaces, we can limit the damage caused by low-level attacks such as
control-flow hijacking. When used to defend against such attacks,
compartmentalization is often implemented cooperatively by a compiler and a
low-level compartmentalization mechanism. However, the formal guarantees
provided by such compartmentalizing compilation have seen surprisingly little
investigation.
We propose a new security property, secure compartmentalizing compilation
(SCC), that formally characterizes the guarantees provided by
compartmentalizing compilation and clarifies its attacker model. We reconstruct
our property by starting from the well-established notion of fully abstract
compilation, then identifying and lifting three important limitations that make
standard full abstraction unsuitable for compartmentalization. The connection
to full abstraction allows us to prove SCC by adapting established proof
techniques; we illustrate this with a compiler from a simple unsafe imperative
language with procedures to a compartmentalized abstract machine.Comment: Nit
High-Fidelity Provenance:Exploring the Intersection of Provenance and Security
In the past 25 years, the World Wide Web has disrupted the way news are disseminated and consumed. However, the euphoria for the democratization of news publishing was soon followed by scepticism, as a new phenomenon emerged: fake news. With no gatekeepers to vouch for it, the veracity of the information served over the World Wide Web became a major public concern. The Reuters Digital News Report 2020 cites that in at least half of the EU member countries, 50% or more of the population is concerned about online fake news. To help address the problem of trust on information communi- cated over the World Wide Web, it has been proposed to also make available the provenance metadata of the information. Similar to artwork provenance, this would include a detailed track of how the information was created, updated and propagated to produce the result we read, as well as what agentsâhuman or softwareâwere involved in the process. However, keeping track of provenance information is a non-trivial task. Current approaches, are often of limited scope and may require modifying existing applications to also generate provenance information along with thei regular output. This thesis explores how provenance can be automatically tracked in an application-agnostic manner, without having to modify the individual applications. We frame provenance capture as a data flow analysis problem and explore the use of dynamic taint analysis in this context. Our work shows that this appoach improves on the quality of provenance captured compared to traditonal approaches, yielding what we term as high-fidelity provenance. We explore the performance cost of this approach and use deterministic record and replay to bring it down to a more practical level. Furthermore, we create and present the tooling necessary for the expanding the use of using deterministic record and replay for provenance analysis. The thesis concludes with an application of high-fidelity provenance as a tool for state-of-the art offensive security analysis, based on the intuition that software too can be misguided by "fake news". This demonstrates that the potential uses of high-fidelity provenance for security extend beyond traditional forensics analysis
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