{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T03:05:16Z","timestamp":1775012716465,"version":"3.50.1"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Virol Hack Tech"],"DOI":"10.1007\/s11416-024-00529-x","type":"journal-article","created":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T15:25:59Z","timestamp":1721748359000},"page":"429-440","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Next gen cybersecurity paradigm towards artificial general intelligence: Russian market challenges and future global technological trends"],"prefix":"10.1007","volume":"20","author":[{"given":"Ekaterina","family":"Pleshakova","sequence":"first","affiliation":[]},{"given":"Aleksey","family":"Osipov","sequence":"additional","affiliation":[]},{"given":"Sergey","family":"Gataullin","sequence":"additional","affiliation":[]},{"given":"Timur","family":"Gataullin","sequence":"additional","affiliation":[]},{"given":"Athanasios","family":"Vasilakos","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"issue":"2","key":"529_CR1","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/0022-0000(84)90070-9","volume":"28","author":"S Goldwasser","year":"1984","unstructured":"Goldwasser, S., Micali, S.: Probabilistic encryption. J. Comput. Syst. Sci. 28(2), 270\u2013299 (1984). https:\/\/doi.org\/10.1016\/0022-0000(84)90070-9","journal-title":"J. Comput. Syst. Sci."},{"key":"529_CR2","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. CoRR, arXiv:1412.6572 (2014)"},{"key":"529_CR3","unstructured":"Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial nets. In Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (Eds.), Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8\u201313 2014, Montreal, Quebec, Canada, pp. 2672\u20132680 (2014). http:\/\/papers.nips.cc\/paper\/5423-generative-adversarial-nets"},{"key":"529_CR4","unstructured":"Xie, P., et al.: Crypto-nets: neural networks over encrypted data. arXiv preprint arXiv:1412.6181 (2014)"},{"key":"529_CR5","unstructured":"Gilad-Bachrach, R., et al. Cryptonets: applying neural networks to encrypted data with high throughput and accuracy. In: International Conference on Machine Learning (PMLR) (2016)"},{"key":"529_CR6","unstructured":"Neelakantan, A., Le, Q.V., Sutskever, I.: Neural programmer: Inducing latent programs with gradient descent. CoRR, arXiv:1511.04834 (2015)"},{"key":"529_CR7","unstructured":"Ruttor, A.: Neural Synchronization and Cryptography. PhD thesis, Julius Maximilian University of Wurzburg (2006). http:\/\/www.opus-bayern.de\/uni-wuerzburg\/volltexte\/2007\/2361\/"},{"key":"529_CR8","doi-asserted-by":"publisher","unstructured":"Klimov, A., Mityagin, A., Shamir, A.: Analysis of neural cryptography. In: Zheng, Y. (Ed.) Advances in Cryptology\u2014ASIACRYPT 2002, 8th International Conference on the Theory and Application of Cryptology and Information Security, Queenstown, New Zealand, December 1\u20135, 2002, Proceedings, volume 2501 of Lecture Notes in Computer Science, pp. 288\u2013298. Springer, Berlin (2002). https:\/\/doi.org\/10.1007\/3-540-36178-2.18","DOI":"10.1007\/3-540-36178-2.18"},{"key":"529_CR9","unstructured":"Deng, G., Liu, Y., Mayoral-Vilches, V., Liu, P., Li, Y., Xu, Y., Zhang, T., Liu, Y., Pinzger, M., Rass, S.: Pentest-gpt: an llm-empowered automatic penetration testing tool. arXiv preprint arXiv:2308.06782 (2023)"},{"key":"529_CR10","doi-asserted-by":"publisher","first-page":"114936","DOI":"10.1109\/ACCESS.2023.3325727","volume":"11","author":"DK Kholgh","year":"2023","unstructured":"Kholgh, D.K., Kostakos, P.: PAC-GPT: a novel approach to generating synthetic network traffic with GPT-3. IEEE Access 11, 114936\u2013114951 (2023). https:\/\/doi.org\/10.1109\/ACCESS.2023.3325727","journal-title":"IEEE Access"},{"key":"529_CR11","doi-asserted-by":"crossref","unstructured":"Yenduri, G., Ramalingam, M., Chemmalar, S.G., Supriya, Y., Gautam, S., Praveen Kumar Reddy, M., Deepti, R.G., Rutvij, H.J., Prabadevi, B., Wang, W., Vasilakos, A.V., Thippa Reddy, G.: Generative pre-trained transformer: a comprehensive review on enabling technologies, potential applications, emerging challenges, and future directions. CoRR arXiv:2305.10435 (2023)","DOI":"10.1109\/ACCESS.2024.3389497"},{"key":"529_CR12","doi-asserted-by":"crossref","unstructured":"Luo, H., Luo, J., Vasilakos, A.V.: BC4LLM: trusted artificial intelligence when blockchain meets large language models. CoRR arXiv:2310.06278 (2023)","DOI":"10.1016\/j.neucom.2024.128089"},{"key":"529_CR13","unstructured":"National Institute of Standards and Technology, U.S. Department of Commerce (NIST), [electronic resource]. https:\/\/www.nist.gov\/cyberframework\/csf-11-archive. Accessed 03\/09\/2024"},{"key":"529_CR14","unstructured":"The Federal Service for Technical and Export Control (FSTEC of Russia), information security threats databank, [electronic resource], https:\/\/bdu.fstec.ru\/threat. Accessed 03\/09\/2024"},{"key":"529_CR15","unstructured":"Information security analytics section, the RF Central Bank [electronic resource]. https:\/\/cbr.ru\/analytics\/ib\/operations_survey\/2023\/. Accessed 03\/05\/2024"},{"key":"529_CR16","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/S1361-3723(20)30050-6","volume":"66","author":"J Richardson","year":"2020","unstructured":"Richardson, J.: Is there a silver bullet to stop cybercrime? Comput. Fraud. Secur. 66, 6\u20138 (2020)","journal-title":"Comput. Fraud. Secur."},{"key":"529_CR17","doi-asserted-by":"publisher","unstructured":"Zuev S.V. Geometric\nproperties of quantum entanglement and machine learning. Russian\nTechnological Journal. 11(5):19\u201333. (2023) https:\/\/doi.org\/10.32362\/2500-316X-2023-11-5-19-33","DOI":"10.32362\/2500-316X-2023-11-5-19-33"},{"key":"529_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102652","volume":"116","author":"A Chevrot","year":"2022","unstructured":"Chevrot, A., Vernotte, A., Legeard, B.: CAE: contextual auto-encoder for multivariate time-series anomaly detection in air transportation. Comput. Secur. 116, 102652 (2022)","journal-title":"Comput. Secur."},{"key":"529_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100402","volume":"40","author":"K Al-Hashedi","year":"2021","unstructured":"Al-Hashedi, K., Magalingam, P.: Financial fraud detection applying data mining techniques: a comprehensive review from 2009 to 2019. Comput. Sci. Rev. 40, 100402 (2021)","journal-title":"Comput. Sci. Rev."},{"key":"529_CR20","doi-asserted-by":"publisher","first-page":"236","DOI":"10.1016\/j.future.2020.08.033","volume":"115","author":"W Feng","year":"2021","unstructured":"Feng, W., Liu, Sh., Cheng, X.: EagleMine: vision-guided Micro-clusters recognition and collective anomaly detection. Future Gener. Comput. Syst. 115, 236\u2013250 (2021)","journal-title":"Future Gener. Comput. Syst."},{"key":"529_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112964","volume":"141","author":"S-Y Shin","year":"2020","unstructured":"Shin, S.-Y., Kang, Y.-W., Kim, Y.-G.: Android-GAN: defending against android pattern attacks using multi-modal generative network as anomaly detector. Expert Syst. Appl. 141, 112964 (2020)","journal-title":"Expert Syst. Appl."},{"key":"529_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.116429","volume":"193","author":"W Hilal","year":"2022","unstructured":"Hilal, W., Gadsden, A., Yawney, J.: Financial fraud: a review of anomaly detection techniques and recent advances. Expert Syst. Appl. 193, 116429 (2022)","journal-title":"Expert Syst. Appl."},{"key":"529_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-023-00500-2","author":"A Osipov","year":"2023","unstructured":"Osipov, A., Pleshakova, E., Liu, Y., et al.: Machine learning methods for speech emotion recognition on telecommunication systems. J. Comput. Virol. Hack Technol. (2023). https:\/\/doi.org\/10.1007\/s11416-023-00500-2","journal-title":"J. Comput. Virol. Hack Technol."},{"key":"529_CR24","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-023-00480-3","author":"V Ivanyuk","year":"2023","unstructured":"Ivanyuk, V.: Forecasting of digital financial crimes in Russia based on machine learning methods. J. Comput. Virol. Hack Technol. (2023). https:\/\/doi.org\/10.1007\/s11416-023-00480-3","journal-title":"J. Comput. Virol. Hack Technol."},{"key":"529_CR25","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-023-00486-x","author":"E Boltachev","year":"2023","unstructured":"Boltachev, E.: Potential cyber threats of adversarial attacks on autonomous driving models. J. Comput. Virol. Hack Technol. (2023). https:\/\/doi.org\/10.1007\/s11416-023-00486-x","journal-title":"J. Comput. Virol. Hack Technol."},{"key":"529_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-023-00492-z","author":"PV Mizinov","year":"2023","unstructured":"Mizinov, P.V., Konnova, N.S., Basarab, M.A., et al.: Parametric study of hand dorsal vein biometric recognition vulnerability to spoofing attacks. J. Comput. Virol. Hack Technol. (2023). https:\/\/doi.org\/10.1007\/s11416-023-00492-z","journal-title":"J. Comput. Virol. Hack Technol."},{"key":"529_CR27","doi-asserted-by":"publisher","unstructured":"Bespalova, N., et al.: Development of a network traffic anomaly detection system based on neural networks. In: Samsonovich, A.V., Liu, T. (Eds.) Biologically Inspired Cognitive Architectures 2023 (BICA 2023). Studies in Computational Intelligence, vol. 1130. Springer, Cham (2024). https:\/\/doi.org\/10.1007\/978-3-031-50381-8_13","DOI":"10.1007\/978-3-031-50381-8_13"},{"key":"529_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-023-00491-0","author":"D Efanov","year":"2023","unstructured":"Efanov, D., Aleksandrov, P., Mironov, I.: Comparison of the effectiveness of cepstral coefficients for Russian speech synthesis detection. J. Comput. Virol. Hack Tech. (2023). https:\/\/doi.org\/10.1007\/s11416-023-00491-0","journal-title":"J. Comput. Virol. Hack Tech."},{"key":"529_CR29","doi-asserted-by":"publisher","first-page":"3164","DOI":"10.3390\/math9243164","volume":"9","author":"A Bykov","year":"2021","unstructured":"Bykov, A., Grecheneva, A., Kuzichkin, O., Surzhik, D., Vasilyev, G., Yerbayev, Y.: Mathematical description and laboratory study of electrophysical methods of localization of geodeformational changes during the control of the railway roadbed. Mathematics 9, 3164 (2021). https:\/\/doi.org\/10.3390\/math9243164","journal-title":"Mathematics"},{"key":"529_CR30","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.jfds.2023.100109","volume":"9","author":"L Garin","year":"2023","unstructured":"Garin, L., Gisin, V.: Machine learning in classifying bitcoin addresses. J. Finance Data Sci. 9, 100\u2013109 (2023)","journal-title":"J. Finance Data Sci."},{"key":"529_CR31","unstructured":"Li, Y., Lyu, S.: Exposing deepfake videos by detecting face warping artifacts. In: CVPR Workshops (2019)"},{"key":"529_CR32","doi-asserted-by":"crossref","unstructured":"Khayatkhoei, M., Elgammal, A: Spatial frequency bias in convolutional generative adversarial networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 7152\u20137159 (2022)","DOI":"10.1609\/aaai.v36i7.20675"},{"key":"529_CR33","unstructured":"Dzanic, T., Shah, K., Witherden, F.D.: Fourier spectrum discrepancies in deep network generated images. In: Proceedings of the 34th International Conference on Neural Information Processing Systems, pp. 3022\u20133032 (2020)"},{"key":"529_CR34","doi-asserted-by":"crossref","unstructured":"Qian, Y., Yin, G., Sheng, L., Chen, Z., Shao, J.: Thinking in frequency: face forgery detection by mining frequency-aware clues. In: European Conference on Computer Vision, pp. 86\u2013103 (2020)","DOI":"10.1007\/978-3-030-58610-2_6"},{"key":"529_CR35","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhou, W., Chen, D., Wei, T., Zhang, W., Yu, N.: Multi-attentional deepfake detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2185\u20132194 (2021)","DOI":"10.1109\/CVPR46437.2021.00222"},{"key":"529_CR36","doi-asserted-by":"crossref","unstructured":"Sebyakin, A., Soloviev, V., Zolotaryuk, A.: Spatio-temporal deepfake detection with deep neural networks. In: International Conference on Information, pp. 78\u201394 (2021)","DOI":"10.1007\/978-3-030-71292-1_8"},{"key":"529_CR37","doi-asserted-by":"crossref","unstructured":"Guo, H., Hu, S., Wang, X., Chang, M.-C., Lyu, S.: Eyes tell all: Irregular pupil shapes reveal gan-generated faces. In: ICASSP 2022\u20142022 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), pp. 2904\u20132908 (2022)","DOI":"10.1109\/ICASSP43922.2022.9746597"},{"key":"529_CR38","doi-asserted-by":"publisher","unstructured":"Le, B.M., Woo, S.S.: Quality-agnostic deepfake detection with intra-model collaborative learning. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France, pp. 22321\u201322332 (2023). https:\/\/doi.org\/10.1109\/ICCV51070.2023.02045","DOI":"10.1109\/ICCV51070.2023.02045"}],"container-title":["Journal of Computer Virology and Hacking Techniques"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11416-024-00529-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11416-024-00529-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11416-024-00529-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,9]],"date-time":"2025-01-09T10:10:30Z","timestamp":1736417430000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11416-024-00529-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,23]]},"references-count":38,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["529"],"URL":"https:\/\/doi.org\/10.1007\/s11416-024-00529-x","relation":{},"ISSN":["2263-8733"],"issn-type":[{"value":"2263-8733","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,23]]},"assertion":[{"value":"26 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}