Node discovery scheme of DDS using dynamic bloom filters

Khaefi, Muhammad Rizal; Kim, Dong-Seong

doi:10.1109/etfa.2014.7005307

Cited by 3 publications

(1 citation statement)

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“…Our work extends this by adding more reachability verification on fnmatch expression to ensure that no covert channels exist in candidate policies. Khaefi et al [11], [12] presented how using a bloom filter in DDS node discovery phase could significantly reduce the payload of handshake traffic at the expense of a tiny chance of collision. Encoding topic discovery data into a bloom filter indeed obscures the topic expressions while providing some probabilistic integrity of the topic permissions.…”

Section: Ddsmentioning

confidence: 99%

Network Reconnaissance and Vulnerability Excavation of Secure DDS Systems

White

Caiazza

Jiang

et al. 2019

2019 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

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Data Distribution Service (DDS) is a realtime peerto-peer protocol that serves as a scalable middleware between distributed networked systems found in many Industrial IoT domains such as automotive, medical, energy, and defense. Since the initial ratification of the standard, specifications have introduced a Security Model and Service Plugin Interface (SPI) architecture, facilitating authenticated encryption and data centric access control while preserving interoperable data exchange. However, as Secure DDS v1.1, the default plugin specifications presently exchanges digitally signed capability lists of both participants in the clear during the crypto handshake for permission attestation; thus breaching confidentiality of the context of the connection. In this work, we present an attacker model that makes use of network reconnaissance afforded by this leaked context in conjunction with formal verification and model checking to arbitrarily reason about the underlying topology and reachability of information flow, enabling targeted attacks such as selective denial of service, adversarial partitioning of the data bus, or vulnerability excavation of vendor implementations.

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Section: Ddsmentioning

confidence: 99%