{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T10:14:36Z","timestamp":1778408076639,"version":"3.51.4"},"publisher-location":"Cham","reference-count":55,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031360954","type":"print"},{"value":"9783031360961","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-36096-1_5","type":"book-chapter","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T16:02:22Z","timestamp":1686844942000},"page":"65-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Generative Methods for\u00a0Out-of-distribution Prediction and\u00a0Applications for\u00a0Threat Detection and\u00a0Analysis: A Short Review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4670-8157","authenticated-orcid":false,"given":"Erica","family":"Coppolillo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9402-7375","authenticated-orcid":false,"given":"Angelica","family":"Liguori","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7711-9833","authenticated-orcid":false,"given":"Massimo","family":"Guarascio","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2922-0835","authenticated-orcid":false,"given":"Francesco Sergio","family":"Pisani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9672-3833","authenticated-orcid":false,"given":"Giuseppe","family":"Manco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,16]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Aghakhani, H., Machiry, A., Nilizadeh, S., Kruegel, C., Vigna, G.: Detecting deceptive reviews using generative adversarial networks. CoRR abs\/1805.10364 (2018)","DOI":"10.1109\/SPW.2018.00022"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: ACCV (2018)","DOI":"10.1007\/978-3-030-20893-6_39"},{"key":"5_CR3","doi-asserted-by":"crossref","unstructured":"Ak\u00e7ay, S., Atapour-Abarghouei, A., Breckon, T.P.: Skip-GANomaly: skip connected and adversarially trained encoder-decoder anomaly detection. In: IJCNN (2019)","DOI":"10.1109\/IJCNN.2019.8851808"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Alfeo, A.L., Cimino, M.G., Manco, G., Ritacco, E., Vaglini, G.: Using an autoencoder in the design of an anomaly detector for smart manufacturing. Pattern Recogn. Lett. 136, 272\u2013278 (2020)","DOI":"10.1016\/j.patrec.2020.06.008"},{"key":"5_CR5","unstructured":"An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability (2015). http:\/\/dm.snu.ac.kr\/static\/docs\/TR\/SNUDM-TR-2015-03.pdf"},{"key":"5_CR6","unstructured":"Bank, D., Koenigstein, N., Giryes, R.: Autoencoders. CoRR (2020)"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Computing Surveys (CSUR) 41, 1\u201372 (2009)","DOI":"10.1145\/1541880.1541882"},{"key":"5_CR8","unstructured":"Chen, H., Jiang, L.: Gan-based method for cyber-intrusion detection. CoRR abs\/1904.02426 (2019)"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Chen, J., Shen, Y., Ali, R.: Credit card fraud detection using sparse autoencoder and generative adversarial network. 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), pp. 1054\u20131059 (2018)","DOI":"10.1109\/IEMCON.2018.8614815"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Chen, J., Sathe, S., Aggarwal, C., Turaga, D.: Outlier detection with autoencoder ensembles. In: SDM (2017)","DOI":"10.1137\/1.9781611974973.11"},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yeo, C.K., Lee, B.S., Lau, C.T.: Autoencoder-based network anomaly detection. In: WTS (2018)","DOI":"10.1109\/WTS.2018.8363930"},{"key":"5_CR12","doi-asserted-by":"publisher","unstructured":"Das, S.: FGAN: federated generative adversarial networks for anomaly detection in network traffic (2022). https:\/\/doi.org\/10.48550\/ARXIV.2203.11106. https:\/\/arxiv.org\/abs\/2203.11106","DOI":"10.48550\/ARXIV.2203.11106"},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Fiore, U., De Santis, A., Perla, F., Zanetti, P., Palmieri, F.: Using generative adversarial networks for improving classification effectiveness in credit card fraud detection. Inf. Sci. 479, 448\u2013455 (2019). https:\/\/doi.org\/10.1016\/j.ins.2017.12.030. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025517311519","DOI":"10.1016\/j.ins.2017.12.030"},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Folino, F., Folino, G., Guarascio, M., Pisani, F., Pontieri, L.: On learning effective ensembles of deep neural networks for intrusion detection. Inf. Fusion 72, 48\u201369 (2021). https:\/\/doi.org\/10.1016\/j.inffus.2021.02.007. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253521000245","DOI":"10.1016\/j.inffus.2021.02.007"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Fu, Y., Lan, Q.: Deep generative model for malware detection. In: 2020 Chinese Control And Decision Conference (CCDC), pp. 2072\u20132077 (2020). https:\/\/doi.org\/10.1109\/CCDC49329.2020.9164231","DOI":"10.1109\/CCDC49329.2020.9164231"},{"key":"5_CR16","unstructured":"Goodfellow, I.J., et al.: Generative adversarial nets. In: NIPS, pp. 2672\u20132680 (2014)"},{"key":"5_CR17","doi-asserted-by":"publisher","unstructured":"Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Studies in Computational Intelligence. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-24797-2","DOI":"10.1007\/978-3-642-24797-2"},{"key":"5_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/978-3-319-66399-9_4","volume-title":"Computer Security \u2013 ESORICS 2017","author":"K Grosse","year":"2017","unstructured":"Grosse, K., Papernot, N., Manoharan, P., Backes, M., McDaniel, P.: Adversarial examples for malware detection. In: Foley, S.N., Gollmann, D., Snekkenes, E. (eds.) ESORICS 2017. LNCS, vol. 10493, pp. 62\u201379. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66399-9_4"},{"key":"5_CR19","first-page":"634","volume":"1\u20133","author":"M Guarascio","year":"2018","unstructured":"Guarascio, M., Manco, G., Ritacco, E.: Deep learning. Encycl. Bioinform. Comput. Biol. ABC Bioinf. 1\u20133, 634\u2013647 (2018)","journal-title":"Encycl. Bioinform. Comput. Biol. ABC Bioinf."},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"Guarascio, M., Cassavia, N., Pisani, F.S., Manco, G.: Boosting cyber-threat intelligence via collaborative intrusion detection. Future Gener. Comput. Syst. 135, 30\u201343 (2022). https:\/\/doi.org\/10.1016\/j.future.2022.04.028. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0167739X22001571","DOI":"10.1016\/j.future.2022.04.028"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Han, X., Chen, X., Liu, L.P.: Gan ensemble for anomaly detection. In: AAAI (2020)","DOI":"10.1609\/aaai.v35i5.16530"},{"key":"5_CR22","unstructured":"Hardy, W., Chen, L., Hou, S., Ye, Y., Li, X.: DL 4 MD : a deep learning framework for intelligent malware detection (2016)"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Hawkins, S., He, H., Williams, G.J., Baxter, R.A.: Outlier detection using replicator neural networks. In: DaWaK (2002)","DOI":"10.1007\/3-540-46145-0_17"},{"issue":"8","key":"5_CR24","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Hsu, C., Lee, C., Zhuang, Y.: Learning to detect fake face images in the wild. CoRR abs\/1809.08754 (2018)","DOI":"10.1109\/IS3C.2018.00104"},{"key":"5_CR26","doi-asserted-by":"publisher","unstructured":"Kim, J.Y., Bu, S.J., Cho, S.B.: Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders. Inf. Sci. 460\u2013461, 83\u2013102 (2018). https:\/\/doi.org\/10.1016\/j.ins.2018.04.092. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025518303475","DOI":"10.1016\/j.ins.2018.04.092"},{"key":"5_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2021.102501","volume":"112","author":"JY Kim","year":"2022","unstructured":"Kim, J.Y., Cho, S.B.: Obfuscated malware detection using deep generative model based on global\/local features. Comput. Secur. 112, 102501 (2022). https:\/\/doi.org\/10.1016\/j.cose.2021.102501","journal-title":"Comput. Secur."},{"key":"5_CR28","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: ICLR (2014)"},{"key":"5_CR29","unstructured":"Laptev, N.: Anogen: Deep anomaly generator (2018). https:\/\/tinyurl.com\/fbanogen"},{"issue":"7553","key":"5_CR30","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Le Cun","year":"2015","unstructured":"Le Cun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"5_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1007\/978-3-030-30490-4_56","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Text and Time Series","author":"D Li","year":"2019","unstructured":"Li, D., Chen, D., Jin, B., Shi, L., Goh, J., Ng, S.-K.: MAD-GAN: multivariate anomaly detection for time series data with generative adversarial networks. In: Tetko, I.V., K\u016frkov\u00e1, V., Karpov, P., Theis, F. (eds.) ICANN 2019. LNCS, vol. 11730, pp. 703\u2013716. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-30490-4_56"},{"key":"5_CR32","doi-asserted-by":"publisher","unstructured":"Liguori, A., Manco, G., Pisani, F.S., Ritacco, E.: Adversarial regularized reconstruction for anomaly detection and generation. In: 2021 IEEE International Conference on Data Mining (ICDM), pp. 1204\u20131209 (2021). https:\/\/doi.org\/10.1109\/ICDM51629.2021.00145","DOI":"10.1109\/ICDM51629.2021.00145"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: ICDM (2008)","DOI":"10.1109\/ICDM.2008.17"},{"key":"5_CR34","unstructured":"Mattia, F.D., Galeone, P., Simoni, M.D., Ghelfi, E.: A survey on GANs for anomaly detection. CoRR (2019)"},{"key":"5_CR35","doi-asserted-by":"publisher","unstructured":"Milenkoski, A., Vieira, M., Kounev, S., Avritzer, A., Payne, B.D.: Evaluating computer intrusion detection systems: a survey of common practices. ACM Comput. Surv. 48(1), 2808691 (2015). https:\/\/doi.org\/10.1145\/2808691","DOI":"10.1145\/2808691"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Ngo, C.P., Winarto, A.A., Li, C.K.K., Park, S., Akram, F., Lee, H.K.: Fence GAN: towards better anomaly detection. In: ICTAI (2019)","DOI":"10.1109\/ICTAI.2019.00028"},{"issue":"1","key":"5_CR37","first-page":"1","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. 54(1), 1\u201338 (2021)","journal-title":"ACM Comput. Surv."},{"key":"5_CR38","doi-asserted-by":"crossref","unstructured":"Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: SIGMOID (2000)","DOI":"10.1145\/342009.335437"},{"key":"5_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1007\/978-3-030-04780-1_28","volume-title":"Big Data Analytics","author":"H Rathore","year":"2018","unstructured":"Rathore, H., Agarwal, S., Sahay, S.K., Sewak, M.: Malware detection using machine learning and deep learning. In: Mondal, A., Gupta, H., Srivastava, J., Reddy, P.K., Somayajulu, D.V.L.N. (eds.) BDA 2018. LNCS, vol. 11297, pp. 402\u2013411. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-04780-1_28"},{"key":"5_CR40","unstructured":"Rizzo, S.G., Pang, L., Chen, Y., Chawla, S.: Probabilistic outlier detection and generation. CoRR (2020)"},{"key":"5_CR41","doi-asserted-by":"crossref","unstructured":"Ruff, L., et al.: A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE (2021)","DOI":"10.1109\/JPROC.2021.3052449"},{"key":"5_CR42","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: NIPS (2016)"},{"key":"5_CR43","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks. Medical Image Analysis 54, 30\u201344 (2019)","DOI":"10.1016\/j.media.2019.01.010"},{"key":"5_CR44","doi-asserted-by":"crossref","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: IPMI (2017)","DOI":"10.1007\/978-3-319-59050-9_12"},{"key":"5_CR45","unstructured":"Sch\u00f6lkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C., et al.: Support vector method for novelty detection. In: NIPS (1999)"},{"key":"5_CR46","unstructured":"Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. CoRR abs\/1708.01967 (2017)"},{"key":"5_CR47","doi-asserted-by":"publisher","unstructured":"Suciu, O., Coull, S., Johns, J.: Exploring adversarial examples in malware detection, pp. 8\u201314 (2019). https:\/\/doi.org\/10.1109\/SPW.2019.00015","DOI":"10.1109\/SPW.2019.00015"},{"key":"5_CR48","doi-asserted-by":"crossref","unstructured":"Tian, K., Zhou, S., Fan, J., Guan, J.: Learning competitive and discriminative reconstructions for anomaly detection. In: AAAI 33 (2019)","DOI":"10.1609\/aaai.v33i01.33015167"},{"key":"5_CR49","unstructured":"Tolstikhin, I., Bousquet, O., Gelly, S., Schoelkopf, B.: Wasserstein auto-encoders. In: ICLR (2019)"},{"key":"5_CR50","unstructured":"Vu, H.S., Ueta, D., Hashimoto, K., Maeno, K., Pranata, S., Shen, S.M.: Anomaly detection with adversarial dual autoencoders. CoRR (2019)"},{"key":"5_CR51","unstructured":"Wang, Q., Guo, W., Zhang, K., Xing, X., Giles, C., Liu, X.: Random feature nullification for adversary resistant deep architecture (2016)"},{"key":"5_CR52","doi-asserted-by":"publisher","unstructured":"Xia, X., et al.: GAN-based anomaly detection: a review. Neurocomputing 493, 497\u2013535 (2022). https:\/\/doi.org\/10.1016\/j.neucom.2021.12.093. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0925231221019482","DOI":"10.1016\/j.neucom.2021.12.093"},{"key":"5_CR53","unstructured":"Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. CoRR (2019)"},{"key":"5_CR54","doi-asserted-by":"crossref","unstructured":"Zenati, H., Romain, M., Foo, C.S., Lecouat, B., Chandrasekhar, V.R.: Adversarially learned anomaly detection. In: ICDM (2018)","DOI":"10.1109\/ICDM.2018.00088"},{"key":"5_CR55","doi-asserted-by":"crossref","unstructured":"Zhou, C., Paffenroth, R.C.: Anomaly detection with robust deep autoencoders. In: KDD (2017)","DOI":"10.1145\/3097983.3098052"}],"container-title":["Communications in Computer and Information Science","Digital Sovereignty in Cyber Security: New Challenges in Future Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36096-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T10:16:41Z","timestamp":1729592201000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36096-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031360954","9783031360961"],"references-count":55,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36096-1_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"16 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CyberSec4Europe","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Digital Sovereignty in Cyber Security: New Challenges in Future Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Venice","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cybersec4europe2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cybersec4europe.eu\/about\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}