Network Traffic Anomaly Detection Using Recurrent Neural Networks

Understand

We show that a recurrent neural network is able to learn a model to represent sequences of communications between computers on a network and can be used to identify outlier network traffic.

• Defending computer networks is a challenging problem and is typically addressed by manually identifying known malicious actor behavior and then specifying rules to recognize such behavior in network communications.
• However, these rule-based approaches often generalize poorly and identify only those patterns that are already known to researchers.
• An alternative approach that does not rely on known malicious behavior patterns can potentially also detect previously unseen patterns.