In today’s fast-paced, ever-changing security environment, only a few minutes can make the difference between a thwarted plot and a national tragedy. More than ever, there’s a need for solutions that can analyze vast datasets as quickly as the threat landscape evolves. Enter the realm of Auto Machine Learning (AutoML), a technology that’s revolutionizing data analytics across various industries. AutoML is an emerging field in artificial intelligence that compresses the end-to-end process of applying machine learning to real-world problems from weeks to hours. In this post, we will discuss how AutoML is transforming the world of national security and defense.

Enhancing Predictive Analytics and Resource Allocation

AutoML technologies reduce the requirement for specialized experts and knowledge in data science by automating the tedious parts of machine learning like feature selection, algorithm selection, hyperparameter tuning, iterative modeling, and model validation. Iterative modeling involves going through the different steps of the model-building process several times, making small changes to the model each time. A data scientist may have to manually perform hundreds of iterations just for a single ML model. With AutoML, predictive models can be developed and deployed more rapidly than ever before. These models can predict potential threats or attacks based on historical data and real-time analysis. AutoML is immensely beneficial in forecasting geopolitical events, predicting cyber threats, and assessing risks, allowing the defense sector to take preemptive measures. When making decisions on resource allocation, AutoML can streamline the process by providing data-driven insights into the most effective allocation strategies. Additionally, cloud-based AutoML platforms like H20 create a lightweight, on-demand machine learning infrastructure that minimizes computing resources and streamlines data analysts’ work.

Increasing Explainability

Many machine learning models lack explainability; that is, they come up with a result but can’t show how they got there. However, national security teams understandably want to know the basis of the model’s results before making mission critical decisions. New AutoML tools not only provide predictions, they also provide information on what features went into that prediction, how important each feature was, and potential sources of model bias. This helps the analyst evaluate whether the model makes sense and is a reliable method of answering the questions at hand before making decisions based on the results. Explainability also provides a source of accountability if analytic missteps occur.

Improving Intelligence Gathering

In the national security and defense sectors, the sheer volume of data generated from diverse sources like satellite images, intelligence reports, social media, cybersecurity logs, and a plethora of other technologies that are constantly generating data is beyond the scale of manual analysis. AutoML can help in processing and analyzing vast amounts of unstructured data, extracting relevant information from different sources like text, audio, video, and images. After integrating this data with platforms like Talend or Link+, AutoML can transform this mass of data into actionable insights. It significantly improves the speed and accuracy of intelligence gathering and interpretation, enabling quicker response to potential threats. Additionally, in intelligence, accounting for the unknown-unknowns (something that we are unaware of that we don’t know) creates a consistent challenge to analysts who seek to find patterns or predict the future. Due to its unsupervised nature, AutoML is in a unique position to uncover these unknown-unknowns and deliver unparalleled insights to decision-makers.

How Praescient Analytics Applies AutoML to Address National Security Issues

Praescient has deployed AutoML tools such as H2O.ai Driverless to address real world missions such as commercial shipping counter narcotics trafficking. In one example, Praescient data scientists configured an anomaly detection algorithm using H2O.ai to flag commercial shipping outliers based on complex mathematical features that would have taken traditional teams weeks to compute. The Driverless platform’s genetic algorithm weighed thousands of possible approaches over the course of a few hours and generated a 98% accurate, explainable model to predict suspicious shipments.

Comparing AutoML model predictions with results from link analysis investigations, Praescient analysts found the AutoML model bolstered some link analysis results while adding new companies of interest not identified by link analysis. Faster speed allows investigators more time to digest findings, as well as communicate to decision-makers and act on these findings. In intelligence analysis, AutoML can cross-check the findings of traditional methods while uncovering the critical unknown-unknowns that other methods neglect.   These applications of AutoML in national security and defense are just the tip of the iceberg. As technology matures further, we can expect even more innovative uses that can help in maintaining international safety and security. Our team at Praescient Analytics is excited to be at the forefront of this technological revolution, committed to delivering robust and efficient AutoML solutions through the combination of data, technology, and tradecraft.