RFP QuestBeta
ClosedStage · contract

Defence Science and Technology Laboratory

Open Call - Autonomous Decision Making for Cyber Defence

IT ServicesCPV 72000000 72200000 72230000 72240000 72260000
Value£952k
Deadline13 Sept 2021
Published5 Nov 2021
RegionNationwide
Timeline
Published 5 Nov 2021ClosedCloses 13 Sept 2021
Contract value in context
£952ktotal contract value
median £120k
this tender£0£3.5m

This is a large award for IT Services — above three-quarters of comparable contracts. Based on 36,449 valued IT Services tenders in our corpus.

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The brief

Under this open call, the Authority is seeking novel Artificial Intelligence (AI) and Machine Learning (ML) approaches for autonomous cyber defence decision making.

Specifically, the Authority is interested in research that aims to: • Develop AI and ML based approaches for autonomous response options planning.

This could include (but is not limited to) the application of reinforcement learning, adversarial machine learning, game theory etc.

Response options could include (but are not limited to): implementation of technical mitigation measures; initiating actions to increase information veracity or certainty before implementing a mitigation response; or initiating actions to identify the cause of system failure in order to recover from it. • Develop multi agent approaches and architectures for cyber defence decision making.

Key aspects include the trade-off between centralised and de-centralised agents, approaches for information sharing between agents, agent hierarchy and multi agent consensus.

Note that this should focus on the interaction of machine agents and not the interaction of humans with machine agents. • Develop methods and approaches to evaluating the decisions generated by the agents to determine their effectiveness and impact.

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Source & provenance
OCID
2af58016-74ea-4081-b659-c5b39081ae3e
Stage
contract · Contract
Source
Contracts Finder
Buyer ref
R1000166607
View the original notice on Contracts Finder

Contains public sector information licensed under the Open Government Licence v3.0. Source data © Crown copyright.

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