The next meeting of the section will take place on Tuesday 17 October 2017 from 14.00 to 16.30 at the Royal Statistical Society, 12 Errol Street, London EC1Y 8LX.
The annual general meeting will be held from 13.40 to 14.00. Following the AGM there will be a joint meeting with the Data Science Section on cyber crime featuring talks by Professor Niall Adams (Imperial College London) and GCHQ. The schedule is below:
14:00 – 14:30 Coffee and Tea
14:30 – 15:30 GCHQ speaker. Title: Data science for security at GCHQ. GCHQ
15:30 – 16:30 Prof. Niall Adams. Title: On Constructing Cyber-Analytics. Department of Mathematics, Imperial College, London
This event is free to attend but registration is required on the following link:
The Government Communications Headquarters (GCHQ)
Title: Data science for security at GCHQ
This talk will give a brief survey of data science for cybersecurity at GCHQ, and some thoughts on longer-term challenges for the statistics community.
Prof. Niall Adams
Department of Mathematics, Imperial College, London
Title: On Constructing Cyber-Analytics
Enterprise network defense is providing great opportunities for the development and deployment of statistical and machine learning methods. Such methods are intended to complement existing defenses, such as firewalls, virus scanners, and intrusion detection systems – which are predominantly signature-based. The role of data analysis methods is to provide enhanced situation awareness, by providing monitoring and alerting mechanisms to detect departures from “normal” behavior. In developing analytics in this context, a variety of challenging problems need to be addressed, including the volume and velocity of the data, high levels of heterogeneity, temporal variation, and more. We review aspects of the problem and characteristics of the various data sources. At present, the vision of jointly modelling various data sources at different levels of network abstraction, appears out of reach due to data volume and timeliness concerns. Instead, we describe a set of novel, and often simple, analytics that operate within different levels of the abstract hierarchy.