# Tiresias: Predicting Security Events Through Deep Learning

## TL;DR

Tiresias employs deep learning, specifically RNNs, to predict future security events on machines, enabling proactive defense by forecasting attacker steps with high precision and stability over time.

## Contribution

This work introduces Tiresias, the first system using RNNs to predict specific future security events based on historical data, surpassing binary attack detection.

## Key findings

- Achieves up to 0.93 precision in event prediction
- Models remain stable over time with retraining triggers
- Long-term memory of RNNs is crucial for accurate predictions

## Abstract

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.