Cyber Security Cooperative Research Centre (CSCRC), together with Data61, the innovation arm of the Commonwealth Scientific and Industrial Research Organisation (CSIRO), and cybersecurity startup Penten, have announced a joint research project that will focus on developing artificial intelligence (AI) enabled cybersecurity defence mechanisms. Under the arrangement announced at D61 Live on Wednesday, Penten will have access to Data61's AI research, which it will use to extend on its existing work to build AI-enabled technology such as "cyber traps" and "decoys". According to Penten CEO Matthew Wilson, using AI will help speed up the creation of cyber traps and make them more realistic. "Our solutions use artificial intelligence to learn the patterns of activity and content from surrounding computers and data. We then use this information to create realistic and believable mimics. This means we can deliver suitable content extremely efficiently, tailored to a customer environment and with minimal effort on the part of the defender," he said.

Social Media has influenced the way people socially connect, interact and opinionize. The growth in technology has enhanced communication and dissemination of information. Unfortunately,many terror groups like jihadist communities have started consolidating a virtual community online for various purposes such as recruitment, online donations, targeting youth online and spread of extremist ideologies. Everyday a large number of articles, tweets, posts, posters, blogs, comments, views and news are posted online without a check which in turn imposes a threat to the security of any nation. However, different agencies are working on getting down this radical content from various online social media platforms. The aim of our paper is to utilise deep learning algorithm in detection of radicalization contrary to the existing works based on machine learning algorithms. An LSTM based feed forward neural network is employed to detect radical content. We collected total 61601 records from various online sources constituting news, articles and blogs. These records are annotated by domain experts into three categories: Radical(R), Non-Radical (NR) and Irrelevant(I) which are further applied to LSTM based network to classify radical content. A precision of 85.9% has been achieved with the proposed approach