"The attacker is poking at the memory controller, the library door, to say, 'is it busy now?' "We were motivated to undertake this work as there was nothing available that could do this kind of automated detecting and killing on a user's machine in real-time." Existing products, known as endpoint detection and response (EDR), are used to protect end-user devices such as desktops, laptops, and mobile devices and are designed to quickly detect, analyse, block, and contain attacks that are in progress. The main problem with these products is that the collected data needs to be sent to administrators in order for a response to be implemented, by which time a piece of malware may already have caused damage. To test the new detection method, the team set up a virtual computing environment to represent a group of commonly used laptops, each running up to 35 applications at the same time to simulate normal behaviour. The AI-based detection method was then tested using thousands of samples of malware.

A computer algorithm that can identify patterns in the social media activity of Islamic State supporters could provide clues about where terrorist attacks are likely to occur. Scientists have found they can spot distinct behavioural patterns in the interactions between groups on social media, and it could even help them predict'lone wolf' attacks'. Social media has been a key tool for organisations like Isis to help them recruit supporters and coordinate their activities. Scientists at the University of Miami have used equations used in physics and chemistry to track the constantly shifting behaviour of supporters of Isis (Isis flag pictured). While law enforcement authorities have attempted to keep track of Isis members using social media, they have tended to focus on monitoring the posts made by individuals.