1,544 research outputs found
Verifiably-safe software-defined networks for CPS
Next generation cyber-physical systems (CPS) are expected to be deployed in domains which require scalability as well as performance under dynamic conditions. This scale and dynamicity will require that CPS communication networks be programmatic (i.e., not requiring manual intervention at any stage), but still maintain iron-clad safety guarantees. Software-defined networking standards like OpenFlow provide a means for scalably building tailor-made network architectures, but there is no guarantee that these systems are safe, correct, or secure. In this work we propose a methodology and accompanying tools for specifying and modeling distributed systems such that existing formal verification techniques can be transparently used to analyze critical requirements and properties prior to system implementation. We demonstrate this methodology by iteratively modeling and verifying an OpenFlow learning switch network with respect to network correctness, network convergence, and mobility-related properties. We posit that a design strategy based on the complementary pairing of software-defined networking and formal verification would enable the CPS community to build next-generation systems without sacrificing the safety and reliability that these systems must deliver
Routing-Verification-as-a-Service (RVaaS): Trustworthy Routing Despite Insecure Providers
Computer networks today typically do not provide any mechanisms to the users
to learn, in a reliable manner, which paths have (and have not) been taken by
their packets. Rather, it seems inevitable that as soon as a packet leaves the
network card, the user is forced to trust the network provider to forward the
packets as expected or agreed upon. This can be undesirable, especially in the
light of today's trend toward more programmable networks: after a successful
cyber attack on the network management system or Software-Defined Network (SDN)
control plane, an adversary in principle has complete control over the network.
This paper presents a low-cost and efficient solution to detect misbehaviors
and ensure trustworthy routing over untrusted or insecure providers, in
particular providers whose management system or control plane has been
compromised (e.g., using a cyber attack). We propose
Routing-Verification-as-a-Service (RVaaS): RVaaS offers clients a flexible
interface to query information relevant to their traffic, while respecting the
autonomy of the network provider. RVaaS leverages key features of
OpenFlow-based SDNs to combine (passive and active) configuration monitoring,
logical data plane verification and actual in-band tests, in a novel manner
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples
Machine Learning has been a big success story during the AI resurgence. One
particular stand out success relates to learning from a massive amount of data.
In spite of early assertions of the unreasonable effectiveness of data, there
is increasing recognition for utilizing knowledge whenever it is available or
can be created purposefully. In this paper, we discuss the indispensable role
of knowledge for deeper understanding of content where (i) large amounts of
training data are unavailable, (ii) the objects to be recognized are complex,
(e.g., implicit entities and highly subjective content), and (iii) applications
need to use complementary or related data in multiple modalities/media. What
brings us to the cusp of rapid progress is our ability to (a) create relevant
and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP
techniques. Using diverse examples, we seek to foretell unprecedented progress
in our ability for deeper understanding and exploitation of multimodal data and
continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International
Conference on Web Intelligence (WI). arXiv admin note: substantial text
overlap with arXiv:1610.0770
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