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Deep learning techniques to detect cybersecurity attacks: a systematic mapping study

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Abstract

Context

Recent years have seen a lot of attention into Deep Learning (DL) techniques used to detect cybersecurity attacks. DL techniques can swiftly analyze massive datasets, and automate the detection and mitigation of a wide variety of cybersecurity attacks with superior results. However, no systematic study exists that summarizes these DL techniques since most studies are informal literature surveys or focus on different subjects.

Objective

To deliver a comprehensive and systematic summary of the existing DL techniques used to detect cybersecurity attacks as they are described in the literature. To identify open challenges for future research.

Method

We conducted a systematic mapping study about DL techniques to detect cybersecurity attacks driven by eleven research questions. We followed existing guidelines when defining our research protocol to increase the repeatability and reliability of our results.

Results

From an initial set of 1839 papers, we identified 116 relevant primary studies, primarily published in the last three years. We investigated multiple aspects of the DL techniques, such as the cybersecurity attack types to detect, their application domains, the programming languages, libraries, operating systems, and frameworks used to implement the DL techniques, the datasets used to train the DL models, the types of research carried out (academic or industrial), the performance of the techniques, and the advantages and disadvantages of each technique. We present a new taxonomy comprising 36 different DL techniques. We identified 14 application domains, eight cybersecurity attacks, and 93 publicly available datasets, among other results.

Conclusions

We provide six lessons learned along with recommendations for future research directions. The most active research areas in DL techniques for the identification of cybersecurity attacks discuss CNN and LSTM techniques. DL techniques in cybersecurity is a rapidly growing and developing research area, with many open challenges, including the lack of (a) research conducted in industrial settings, (b) real-time datasets, (c) studies focusing on promising DL techniques and relevant cybersecurity attacks.

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Data Availability

All data generated or analysed during this study are included in this published article (and its supplementary information files).

Notes

  1. https://www.itu.int/en/ITU-T/studygroups/com17/Pages/cybersecurity.aspx – last access on January 31, 2022

  2. https://csrc.nist.gov/glossary/term/Cyber_Attack

  3. We will provide the link to the replication package once the manuscript is accepted for publication.

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Acknowledgements

This research was supported in part by the Air Force Research Laboratory (AFRL) and Department of Homeland Security (DHS) Science and Technology (S&T) Directorate under award FA8750-19-C-0077.

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    Damiano Torre, Frantzy Mesadieu & Anitha Chennamaneni

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Appendix: A

Appendix: A

In this appendix, we present the complete list of 93 datasets used by the authors of the 116 primary studies discussed in this work. Table 19 describes the Name of the dataset (first column), the Year when the dataset was created and or released (second column), the cybersecurity attacks (i.e., CA) involved in each dataset as described in the primary studies (third column), the Domain in which the dataset was applied (fourth column), the link to access the dataset (fifth column), the number of files (NF) included in the dataset (e.g., csv, cdf, accdb files) (sixth column), the dataset attacks (DA) types covered by the dataset as described by the author of the dataset (seventh column), the size (in terms of the number of records) and/or the features and/or the number of applications involved (S/F/A) in each dataset (when these details are provided) (eighth column), and the quality (Q) of the dataset scored as poor (P), fair (F), and good (G) (ninth column). In order to provide a score for each dataset, we considered: (i) how well is the dataset maintained, (ii) the size of it, (iii) how easy is to access it, and (iv) the different types of attacks that cover. Our recommendation in order to properly use the list of 93 datasets is the following: the reader should (a) first look first the application domains of interest (i.e., Domain), (b) second look a the types of cybersecurity attacks covered by each dataset (i.e., CA and DA), and (c) finally look at the score we provide as an ultimate indication. Some of the datasets report NA as the number of files when the files are incrementally added monthly (e.g., NXDomain), NA as size and/or features, when these details are not shared and the amount of files is too large to summarize them, or when the dataset is continuously updated.

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Torre, D., Mesadieu, F. & Chennamaneni, A. Deep learning techniques to detect cybersecurity attacks: a systematic mapping study. Empir Software Eng 28, 76 (2023). https://doi.org/10.1007/s10664-023-10302-1

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