  We introduce a method for Intrusion Detection based on the classification,
understanding and prediction of behavioural deviance and potential threats,
issuing recommendations, and acting to address eminent issues. Our work seeks a
practical solutions to automate the process of identification and response to
Cybersecurity threats in hybrid Distributed Computing environments through the
analysis of large datasets generated during operations. We are motivated by the
growth in utilisation of Cloud Computing and Edge Computing as the technology
for business and social solutions. The technology mix and complex operation
render these environments target to attacks like hijacking, man-in-the-middle,
denial of service, phishing, and others. The Autonomous Intrusion Response
System implements innovative models of data analysis and context-aware
recommendation systems to respond to attacks and self-healing. We introduce a
proof-of-concept implementation and evaluate against datasets from
experimentation scenarios based on public and private clouds. The results
present significant improvement in response effectiveness and potential to
scale to large environments.
