A REVIEW OF GENERATIVE ADVERSARIAL NETWORKS FOR SECURITY APPLICATIONS

Abadi M., Andersen D. G..: Learning to Protect Communications with Adversarial Neural Cryptography. 2016 [http://arxiv.org/abs/1610.06918].
  Google Scholar

Araba Vander–Pallen M. et al.: Survey on Types of Cyber Attacks on Operating System Vulnerabilities since 2018 Onwards. IEEE World AI IoT Congress – AIIoT, 2022, 01–07 [https://doi.org/10.1109/AIIoT54504.2022.9817246].
  Google Scholar

Creech G., Hu J.: A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns. IEEE Transactions on Computers 63(4), 2014, 807–819 [https://doi.org/10.1109/TC.2013.13].
  Google Scholar

Creech G., Hu J.: Generation of a New IDS Test Dataset: Time to Retire the KDD Collection. IEEE Wireless Communications and Networking Conference – WCNC, 2013 [https://doi.org/10.1109/wcnc.2013.6555301].
  Google Scholar

Creech G.: Developing a High-Accuracy Cross Platform Host-Based Intrusion Detection System Capable of Reliably Detecting Zero-Day Attacks. 2014.
  Google Scholar

Dash A. et al.: A Review of Generative Adversarial Networks (GANs) and Its Applications in a Wide Variety of Disciplines - From Medical to Remote Sensing, 2021 [http://arxiv.org/abs/2110.01442].
  Google Scholar

Deng Li.: The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]. IEEE Signal Processing Magazine 29(6), 2012, 141–142 [https://doi.org/10.1109/MSP.2012.2211477].
  Google Scholar

Gomez A. N. et al.: Unsupervised Cipher Cracking Using Discrete GANs, 2018 [http://arxiv.org/abs/1801.04883].
  Google Scholar

Goodfellow I. J. et al.: Generative Adversarial Networks. arXiv [Stat.ML], 2014 [http://arxiv.org/abs/1406.2661].
  Google Scholar

Hitaj B. et al.: PassGAN: A Deep Learning Approach for Password Guessing. ACNS. 2019.
  Google Scholar

Khelifi L., Mignotte M.: Deep Learning for Change Detection in Remote Sensing Images: Comprehensive Review and Meta-Analysis. IEEE Access 8, 2020, 126385–126400 [https://doi.org/10.1109/ACCESS.2020.3008036].
  Google Scholar

Kumar S. et al.: Research Trends in Network-Based Intrusion Detection Systems: A Review. IEEE Access 9, 2021, 157761–157779 [https://doi.org/10.1109/ACCESS.2021.3129775].
  Google Scholar

Learned-Miller G. B. H. E.: Labeled Faces in the Wild: Updates and New Reporting Procedures. University of Massachusetts. 2014.
  Google Scholar

Lin Z. et al.: IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection. Lecture Notes in Computer Science, Springer International Publishing, 2022, 79–91 [https://doi.org/10.1007/978-3-031-05981-0_7].
  Google Scholar

Liu Z. et al.: Deep Learning Face Attributes in the Wild. IEEE International Conference on Computer Vision – ICCV, IEEE, 2015 [https://doi.org/10.1109/iccv.2015.425].
  Google Scholar

Moustafa N., Slay J.: The Evaluation of Network Anomaly Detection Systems: Statistical Analysis of the UNSW-NB15 Data Set and the Comparison with the KDD99 Data Set. Information Security Journal A Global Perspective 25(1–3), 2016, 18–31 [https://doi.org/10.1080/19393555.2015.1125974].
  Google Scholar

Nam S. et al.: Recurrent GANs Password Cracker for IoT Password Security Enhancement. Sensors (Basel, Switzerland) 20(11), 3106 [https://doi.org/10.3390/s20113106].
  Google Scholar

Odena A. et al.: Conditional Image Synthesis With Auxiliary Classifier GANs. 2016 [https://doi.org/10.48550].
  Google Scholar

Radford A. et al.: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. 2016 [http://arxiv.org/abs/1511.06434].
  Google Scholar

Schlegl Th. et al.: Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. 2017 [http://arxiv.org/abs/1703.05921].
  Google Scholar

Sharafaldin I. et al.: Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization. 4th International Conference on Information Systems Security and Privacy (ICISSP), 2018.
  Google Scholar

Shen Y. et al.: Gan-Based Garment Generation Using Sewing Pattern Images. European Conference on Computer Vision, Springer, 225–247.
  Google Scholar

Subramanian, N. et al.: Image Steganography: A Review of the Recent Advances. IEEE Access: Practical Innovations, Open Solutions 9, Institute of Electrical and Electronics Engineers (IEEE), 2021, 23409–23423 [https://doi.org/10.1109/access.2021.3053998].
  Google Scholar

Tavallaee, M. et al.: A Detailed Analysis of the KDD CUP 99 Data Set. IEEE Symposium on Computational Intelligence for Security and Defense Applications, IEEE, 2009 [https://doi.org/10.1109/cisda.2009.5356528].
  Google Scholar

Tong K. et al.: Recent Advances in Small Object Detection Based on Deep Learning: A Review. Image and Vision Computing 97, 2020, 103910 [https://doi.org/10.1016/j.imavis.2020.103910].
  Google Scholar

Volkhonskiy Denis et al.: Steganographic Generative Adversarial Networks. 2019 [http://arxiv.org/abs/1703.05502].
  Google Scholar

Wu Ch. et al.: WGAN-E: A Generative Adversarial Networks for Facial Feature Security. Electronics 9(3), 2020, 486 [https://doi.org/10.3390/electronics9030486].
  Google Scholar

Yang J. et al.: Spatial Image Steganography Based on Generative Adversarial Network, 2018 [http://arxiv.org/abs/1804.07939].
  Google Scholar

Yang Y. et al.: GAN-Based Semi-Supervised Learning Approach for Clinical Decision Support in Health-IoT Platform. IEEE Access 7, 2019, 8048–8057 [https://doi.org/10.1109/ACCESS.2018.2888816].
  Google Scholar

Zenati H. et al.: Efficient GAN-Based Anomaly Detection. 2019. [http://arxiv.org/abs/1802.06222].
  Google Scholar

Zheng W. et al.: GAN-Based Key Secret-Sharing Scheme in Blockchain. IEEE Transactions on Cybernetics 51(1), 2021, 393–404 [https://doi.org/10.1109/TCYB.2019.2963138].
  Google Scholar

Zhu J. Y. et al. Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks, 2017 [https://doi.org/10.48550/ARXIV.1703.10593].
  Google Scholar