BT is deploying an AI trained on epidemiological modelling to fight the increasing risk of cyberattacks.

The first mathematical epidemic model was formulated and solved by Daniel Bernoulli in 1760 to evaluate the effectiveness of variolation of healthy people with the smallpox virus. More recently, such models have guided COVID-19 responses to keep the health and economic damage from the pandemic as minimal as possible.

Now security researchers from BT Labs in Suffolk want to harness centuries of epidemiological modelling advancements to protect networks.

BT’s new epidemiology-based cybersecurity prototype is called Inflame and uses deep reinforcement learning to help enterprises automatically detect and respond to cyberattacks before they compromise a network.

Howard Watson, Chief Technology Officer at BT, said:

“We know the risk of cyberattack is higher than ever and has intensified significantly during the pandemic. Enterprises now need to look to new cybersecurity solutions that can understand the risk and consequence of an attack, and quickly respond before it’s too late.

Epidemiological testing has played a vital role in curbing the spread of infection during the pandemic, and Inflame uses the same principles to understand how current and future digital viruses spread through networks.

Inflame will play a key role in how BT’s Eagle-i platform automatically predicts and identifies cyber-attacks before they impact, protecting customers’ operations and reputation.” 

The ‘R’ rate – used for indicating the estimated rate of further infection per case – has gone from the lexicons of epidemiologists to public knowledge over the course of the pandemic. Alongside binge-watching Tiger King, a lockdown pastime for many of us was to check the latest R rate in the hope that it had dropped below 1—meaning the spread of COVID-19 was decreasing rather than increasing exponentially.

For its Inflame prototype, BT’s team built models that were used to test numerous scenarios based on differing R rates of cyber-infection.

Inflame can automatically model and respond to a detected threat within an enterprise network thanks to its deep reinforcement training.

Responses are underpinned by “attack lifecycle” modelling – similar to understanding the spread of a biological virus – to determine the current stage of a cyberattack by assessing real-time security alerts against recognised patterns. The ability to predict the next stage of a cyberattack helps with determining the best steps to halt its progress.

Last month, BT announced its Eagle-i platform which uses AI for real-time threat detection and intelligent response. Eagle-i “self-learns” from every intervention to constantly improve its threat knowledge and Inflame will be a key component in further improving the platform.

(Photo by Erik Mclean on Unsplash)

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Tags: ai, artificial intelligence, bt, cyberattack, cybersecurity, deep learning, eagle-i, hacking, inflame, platform, reinforcement learning, security