Training a quantum annealing based restricted Boltzmann machine on cybersecurity data

TL;DR

This paper demonstrates the application of quantum annealing to train a restricted Boltzmann machine on cybersecurity data, showing improved classification accuracy and the potential for practical quantum machine learning solutions.

Contribution

It introduces a novel approach of using quantum annealing for RBM training on real-world cybersecurity data, and compares its effectiveness with classical methods.

Findings

Quantum annealing-trained RBM can generate useful synthetic data.

Majority voting improves classification accuracy significantly.

Quantum annealing-based RBM shows potential for practical machine learning applications.

Abstract

We present a real-world application that uses a quantum computer. Specifically, we train a RBM using QA for cybersecurity applications. The D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs are also trained using CD. CD is a commonly used method for RBM training. Our analysis of the ISCX data shows that the dataset is imbalanced. We present two different schemes to balance the training dataset before feeding it to a classifier. The first scheme is based on the undersampling of benign instances. The imbalanced training dataset is divided into five sub-datasets that are trained separately. A majority voting is then performed to get the result. Our results show the majority vote increases the classification accuracy up from 90.24% to 95.68%, in the case of CD. For the case of QA, the classification accuracy increases from 74.14% to 80.04%. In the second scheme, a RBM is used to generate synthetic data to balance the training dataset. We show that both QA and CD-trained RBM can be used to generate useful synthetic data. Balanced training data is used to evaluate several classifiers. Among the classifiers investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) perform better than other classifiers. They both show an accuracy of 93%. Our results show a proof-of-concept that a QA-based RBM can be trained on a 64-bit binary dataset. The illustrative example suggests the possibility to migrate many practical classification problems to QA-based techniques. Further, we show that synthetic data generated from a RBM can be used to balance the original dataset.