Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data

Dixit, Vivek; Selvarajan, Raja; Aldwairi, Tamer; Koshka, Yaroslav; Novotny, Mark A.; Humble, Travis S.; Alam, Muhammad A.; Kais, Sabre

doi:10.1109/tetci.2021.3074916

Title: Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data

Abstract

A restricted Boltzmann machine (RBM) is a generative model that could be used in effectively balancing a cybersecurity dataset because the synthetic data a RBM generates follows the probability distribution of the training data. RBM training can be performed using contrastive divergence (CD) and quantum annealing (QA). QA-based RBM training is fundamentally different from CD and requires samples from a quantum computer. 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%, inmore »« less

Authors:

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Purdue University, West Lafayette, IN (United States)
Temple Univ., Philadelphia, PA (United States)
Mississippi State Univ., Mississippi State, MS (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Quantum Computing Institute

Publication Date:: Wed Jun 01 00:00:00 EDT 2022

Research Org.:: Purdue Univ., West Lafayette, IN (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Basic Energy Sciences (BES)

OSTI Identifier:: 1871582

Alternate Identifier(s):: OSTI ID: 1870258

Grant/Contract Number:: SC0019215; AC05-00OR22725

Resource Type:: Accepted Manuscript

Journal Name:: IEEE Transactions on Emerging Topics in Computational Intelligence

Additional Journal Information:: Journal Volume: 6; Journal Issue: 3; Journal ID: ISSN 2471-285X

Publisher:: IEEE

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Dixit, Vivek, Selvarajan, Raja, Aldwairi, Tamer, Koshka, Yaroslav, Novotny, Mark A., Humble, Travis S., Alam, Muhammad A., and Kais, Sabre. Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data.  United States: N. p., 2022. 
Web.  doi:10.1109/tetci.2021.3074916.

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                    Dixit, Vivek, Selvarajan, Raja, Aldwairi, Tamer, Koshka, Yaroslav, Novotny, Mark A., Humble, Travis S., Alam, Muhammad A., & Kais, Sabre. Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data.  United States.  https://doi.org/10.1109/tetci.2021.3074916

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                    Dixit, Vivek, Selvarajan, Raja, Aldwairi, Tamer, Koshka, Yaroslav, Novotny, Mark A., Humble, Travis S., Alam, Muhammad A., and Kais, Sabre. Wed .  
"Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data".  United States.  https://doi.org/10.1109/tetci.2021.3074916.  https://www.osti.gov/servlets/purl/1871582.

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@article{osti_1871582,

  title        = {Training a Quantum Annealing Based Restricted Boltzmann Machine on Cybersecurity Data},

  author       = {Dixit, Vivek and Selvarajan, Raja and Aldwairi, Tamer and Koshka, Yaroslav and Novotny, Mark A. and Humble, Travis S. and Alam, Muhammad A. and Kais, Sabre},

  abstractNote = {A restricted Boltzmann machine (RBM) is a generative model that could be used in effectively balancing a cybersecurity dataset because the synthetic data a RBM generates follows the probability distribution of the training data. RBM training can be performed using contrastive divergence (CD) and quantum annealing (QA). QA-based RBM training is fundamentally different from CD and requires samples from a quantum computer. 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.},

  doi          = {10.1109/tetci.2021.3074916},

  journal      = {IEEE Transactions on Emerging Topics in Computational Intelligence},

  number       = 3,

  volume       = 6,

  place        = {United States},

  year         = {Wed Jun 01 00:00:00 EDT 2022},

  month        = {Wed Jun 01 00:00:00 EDT 2022}

}

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