Guaranteeing spoof-resilient multi-robot networks

Gil, Stephanie; Kumar, Swarun; Mazumder, Mark; Katabi, Dina; Rus, Daniela

doi:10.1007/s10514-017-9621-5

Cited by 61 publications

(20 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The communications vulnerabilities of the robotics system that were raised in earlier papers can be mitigated using ROS 2 with DDS security; however, this is only a part of developing a secure robotic system. [39][40][41] We note that future work can be done to address the overall security of robotic systems. ROS 2 security is a start, but as shown from a holistic point of view many levels remain exposed.…”

Section: Discussionmentioning

confidence: 99%

Robot Operating System 2

DiLuoffo

Michalson

Sunar

2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

It is no secret that robotic systems are expanding into many human roles or are augmenting human roles. The Robot Operating System is an open-source standard for the robotic industry that enables locomotion, manipulation, navigation, and recognition tasks by integrating sensors, motors, and controllers into reusable modules over a distributed messaging architecture. As reliance on robotic systems increases, these systems become high value targets, for example, in autonomous vehicles where human life is at risk. As Robot Operating System has become a de facto standard for many robotic systems, the security of Robot Operating System becomes an important consideration for deployed systems. The original Robot Operating System implementations were not designed to mitigate the security risks associated with hostile actors. Robot Operating System 2, the next generation of the Robot Operating System, addresses this shortcoming, leveraging Data Distributed Services for its messaging architecture and Data Distributed Services security extension for its data protection in motion. This article provides a systematic review of Robot Operating System 2 and identifies potential risks for this new robotic system paradigm. A Robot Operating System 2 robotic system is viewed as a series of layers from the hardware that include sensors, motors, and controllers to the software layers, which include the operating system, security services, protocols, messaging, and the cognitive layer for observation, learning, and action. Since Robot Operating System 2 and security are new considerations for robotics systems as they move into mainstream, many questions emerge. For example, can some portions be secure and other portions be non-secure? Does everything need to be secure? What are the trade-offs between, security, performance, latency and throughput? What about real-time robotic systems? This article provides an overview of the Robot Operating System 2 paradigm and represents a first step toward answering these questions.

show abstract

Section: Discussionmentioning

confidence: 99%

Robot Operating System 2

DiLuoffo

Michalson

Sunar

2018

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…The other systems that rely on wireless messages are susceptible to Sybil attacks: without a trusted third-party, it is always possible for a malicious agent to create an unlimited number of new identities in peer-to-peer networks (Douceur, 2002 ). Through this large number of identities, an attacker can gain a disproportionate amount of power (Gil et al, 2017 ), potentially causing much damage, e.g., in voting scenarios. The blockchain can prevent Sybil attacks from disrupting swarm behavior by introducing scarcity to decentralized systems: a robot wanting to exert influence must pay for this by spending a scarce resource (cryptotokens).…”

Section: Related Workmentioning

confidence: 99%

Blockchain Technology Secures Robot Swarms: A Comparison of Consensus Protocols and Their Resilience to Byzantine Robots

2020

View full text Add to dashboard Cite

Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology. Bitcoin and later blockchain frameworks, such as Ethereum, have revolutionized financial transactions. These frameworks are based on decentralized databases (blockchains) that can achieve secure consensus in peer-to-peer networks. We illustrate our approach in a collective sensing scenario where robots in a swarm are controlled via blockchain-based smart contracts (decentralized protocols executed via blockchain technology) that serve as “meta-controllers” and we compare it to state-of-the-art consensus protocols using a robot swarm simulator. Additionally, we show that our blockchain-based approach can prevent attacks where robots forge a large number of identities (Sybil attacks). The developed robot-blockchain interface is released as open-source software in order to facilitate future research in blockchain-controlled robot swarms. Besides increasing security, we expect the presented approach to be important for data analysis, digital forensics, and robot-to-robot financial transactions in robot swarms.

show abstract

“…An alternative defense is to use measurements of communication latency or wireless signal strength to verify the uniqueness of IDs [26,38,53,91,140]. However, these techniques are sensitive to measurement accuracy.…”

Section: Distributed Hash Tablesmentioning

confidence: 99%

Resource Burning for Permissionless Systems (Invited Paper)

Gupta

Saia

Young

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Proof-of-work puzzles and CAPTCHAS consume enormous amounts of energy and time. These techniques are examples of resource burning: verifiable consumption of resources solely to convey information.Can these costs be eliminated? It seems unlikely since resource burning shares similarities with "money burning" and "costly signaling", which are foundational to game theory, biology, and economics. Can these costs be reduced? Yes, research shows we can significantly lower the asymptotic costs of resource burning in many different settings.In this paper, we survey the literature on resource burning; take positions based on predictions of how the tool is likely to evolve; and propose several open problems targeted at the theoretical distributed-computing research community.

show abstract

Guaranteeing spoof-resilient multi-robot networks

Cited by 61 publications

References 44 publications

Robot Operating System 2

Robot Operating System 2

Blockchain Technology Secures Robot Swarms: A Comparison of Consensus Protocols and Their Resilience to Byzantine Robots

Resource Burning for Permissionless Systems (Invited Paper)

Contact Info

Product

Resources

About