Researchers Use Machine Learning Techniques to Detect Compromised Network Accounts on the Pure AI

I contributed to an article titled “Researchers Use Machine Learning Techniques to Detect Compromised Network Accounts” on the Pure AI web site. See

The article describes how researchers and engineers (including me) developed a successful system that detects compromised enterprise network user accounts. The effort is named Project Qidemon. The core of Qidemon is a deep neural autoencoder architecture. In one experiment with real life data, Qidemon examined 20,000 user accounts and successfully found seven of 13 known compromised accounts.

In many scenarios, network users have broad permissions to view and modify resources. A single instance of a malicious network internal event can have catastrophic consequences for a company.

A deep neural autoencoder accepts input, such as network user login activity, and learns a model that predicts its input. The difference between actual input and computed output is called reconstruction error. The fundamental idea is that inputs that have high reconstruction error are anomalous, and therefore accounts associated with those inputs warrant close inspection. Internally, an autoencoder creates a compressed representation of its source data.

For example, suppose an input to a deep neural autoencoder is (time = 21:30:00, recent failed attempts = 1, IP address ID = 73027). A prediction output such as (time = 21:27:35, recent failed attempts = 1.5, IP address ID = 73027) would have low reconstruction error, but a prediction output such as (time = 06:25:00, recent failed attempts = 3.5, IP address ID = 99999) would have high reconstruction error.

The Qidemon system was compared to a system that used Principal Component Analysis (PCA). PCA is a classical statistics technique that is somewhat similar to a deep neural autoencoder in the sense that both models create a compressed representation of their source data. The compressed representation can be used to reconstruct the source data and so a reconstruction error metric can be calculated. Experiments showed that the Qidemon anomaly detection system significantly outperforms the PCA-based system.

I was quoted in the article:

McCaffrey noted that, “Deep neural systems have had amazing successes in areas such as image recognition, speech and natural language processing, but progress in security systems has been slower. In my opinion, this project represents a significant step forward.”

When playing poker, players who have anomalous hands — very good hands like a Full House or very bad hands like a Pair of Fours — try not to reveal anything with their expressions. This is called a poker face. Three images from the Internet of literal poker faces.

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