Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task

Real-time detection of grid bulk transfer traffic

The current practice of physical science research has yielded a continuously growing demand for interconnection network bandwidth to support the sharing of large datasets. Academic research networks and internet service providers have provisioned their networks to handle this type of load, which generates prolonged, high-volume traffic between nodes on the network. Maintenance of QoS for all network users demands that the onset of these (Grid bulk) transfers be detected to enable them to be reengineered through resources specifically provisioned to handle this type of traffic. This paper describes a real-time detector that operates at full-line-rate on Gb/s links, operates at high connection rates, and can track the use of ephemeral or non-standard ports