{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,11]],"date-time":"2026-06-11T16:19:38Z","timestamp":1781194778676,"version":"3.54.1"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031295034","type":"print"},{"value":"9783031295041","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\/978-3-031-29504-1_9","type":"book-chapter","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T10:23:50Z","timestamp":1680517430000},"page":"156-174","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["FlowADGAN: Adversarial Learning for\u00a0Deep Anomaly Network Intrusion Detection"],"prefix":"10.1007","author":[{"given":"Pan","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeyi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaokang","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhua","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weizheng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,4,4]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","unstructured":"Abolhasanzadeh, B.: Nonlinear dimensionality reduction for intrusion detection using auto-encoder bottleneck features. In: 2015 7th Conference on Information and Knowledge Technology (IKT), pp. 15 (2015). https:\/\/doi.org\/10.1109\/IKT.2015.7288799","DOI":"10.1109\/IKT.2015.7288799"},{"key":"9_CR2","doi-asserted-by":"publisher","unstructured":"Ahmed, M., Naser Mahmood, A., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60(C), 19\u201331 (2016). https:\/\/doi.org\/10.1016\/j.jnca.2015.11.016","DOI":"10.1016\/j.jnca.2015.11.016"},{"key":"9_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1007\/978-3-030-20893-6_39","volume-title":"Computer Vision \u2013 ACCV 2018","author":"S Akcay","year":"2019","unstructured":"Akcay, S., Atapour-Abarghouei, A., Breckon, T.P.: GANomaly: semi-supervised anomaly detection via adversarial training. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11363, pp. 622\u2013637. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20893-6_39"},{"key":"9_CR4","doi-asserted-by":"publisher","unstructured":"Amer, M., Goldstein, M., Abdennadher, S.: Enhancing one-class support vector machines for unsupervised anomaly detection. In: Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, ODD\u201913, pp. 8\u201315. Association for Computing Machinery, New York (2013). https:\/\/doi.org\/10.1145\/2500853.2500857","DOI":"10.1145\/2500853.2500857"},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: SIGMOD, vol. 29, no, 2, pp. 93\u2013104 (2000)","DOI":"10.1145\/335191.335388"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of data, pp. 93\u2013104 (2000)","DOI":"10.1145\/342009.335388"},{"key":"9_CR7","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.cose.2016.02.008","volume":"59","author":"J Camacho","year":"2016","unstructured":"Camacho, J., P\u00e9rez-Villegas, A., Garc\u00eda-Teodoro, P., Maci\u00e1-Fern\u00e1ndez, G.: PCA-based multivariate statistical network monitoring for anomaly detection. Comput. Secur. 59, 118\u2013137 (2016)","journal-title":"Comput. Secur."},{"issue":"3","key":"9_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 1\u201358 (2009). https:\/\/doi.org\/10.1145\/1541880.1541882","journal-title":"ACM Comput. Surv."},{"issue":"1","key":"9_CR9","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1109\/MSP.2017.2765202","volume":"35","author":"A Creswell","year":"2018","unstructured":"Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53\u201365 (2018)","journal-title":"IEEE Signal Process. Mag."},{"key":"9_CR10","doi-asserted-by":"publisher","unstructured":"Depren, O., Topallar, M., Anarim, E., Ciliz, M.K.: An intelligent intrusion detection system (ids) for anomaly and misuse detection in computer networks. Expert Syst. Appl. 29(4), 713\u2013722 (2005). https:\/\/doi.org\/10.1016\/j.eswa.2005.05.002, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0957417405000989","DOI":"10.1016\/j.eswa.2005.05.002"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Falc\u00e3o, F., et al.: Quantitative comparison of unsupervised anomaly detection algorithms for intrusion detection. In: Proceedings of the 34th ACM\/SIGAPP Symposium on Applied Computing, SAC\u201919, pp. 318\u2013327. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3297280.3297314","DOI":"10.1145\/3297280.3297314"},{"key":"9_CR12","unstructured":"Gharib, M., Mohammadi, B., Dastgerdi, S.H., Sabokrou, M.: Autoids: auto-encoder based method for intrusion detection system. ArXiv abs\/1911.03306 (2019)"},{"key":"9_CR13","doi-asserted-by":"publisher","unstructured":"Kriegel, H.P., Schubert, M., Zimek, A.: Angle-based outlier detection in high-dimensional data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD\u201908, pp. 444\u2013452. Association for Computing Machinery, New York (2008). https:\/\/doi.org\/10.1145\/1401890.1401946","DOI":"10.1145\/1401890.1401946"},{"key":"9_CR14","doi-asserted-by":"publisher","unstructured":"Kwitt, R., Hofmann, U.: Unsupervised anomaly detection in network traffic by means of robust PCA. In: 2007 International Multi-Conference on Computing in the Global Information Technology (ICCGI\u201907), p. 37 (2007). https:\/\/doi.org\/10.1109\/ICCGI.2007.62","DOI":"10.1109\/ICCGI.2007.62"},{"key":"9_CR15","unstructured":"Li, K.L., Huang, H.K., Tian, S.F., Xu, W.: Improving one-class SVM for anomaly detection. In: Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 03EX693), vol. 5, pp. 3077\u20133081. IEEE (2003)"},{"key":"9_CR16","doi-asserted-by":"publisher","unstructured":"Liu, F.T., Ting, K.M., Zhou, Z.H.: Isolation forest. In: 2008 8th IEEE International Conference on Data Mining, pp. 413\u2013422 (2008). https:\/\/doi.org\/10.1109\/ICDM.2008.17","DOI":"10.1109\/ICDM.2008.17"},{"issue":"2","key":"9_CR17","doi-asserted-by":"publisher","first-page":"1913","DOI":"10.1109\/TNSM.2020.3038991","volume":"18","author":"S Longari","year":"2021","unstructured":"Longari, S., Nova Valcarcel, D.H., Zago, M., Carminati, M., Zanero, S.: Cannolo: An anomaly detection system based on LSTM autoencoders for controller area network. IEEE Trans. Netw. Serv. Manage. 18(2), 1913\u20131924 (2021). https:\/\/doi.org\/10.1109\/TNSM.2020.3038991","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. ArXiv abs\/1802.09089 (2018)","DOI":"10.14722\/ndss.2018.23204"},{"key":"9_CR19","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. arXiv preprint. arXiv:1802.09089 (2018)","DOI":"10.14722\/ndss.2018.23204"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1\u20136. IEEE (2015)","DOI":"10.1109\/MilCIS.2015.7348942"},{"issue":"2","key":"9_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","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(2), 1\u201338 (2021). https:\/\/doi.org\/10.1145\/3439950","journal-title":"ACM Comput. Surv."},{"issue":"2","key":"9_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","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. (CSUR) 54(2), 1\u201338 (2021)","journal-title":"ACM Comput. Surv. (CSUR)"},{"issue":"18","key":"9_CR23","doi-asserted-by":"publisher","first-page":"4203","DOI":"10.1002\/sec.1335","volume":"8","author":"N Paulauskas","year":"2015","unstructured":"Paulauskas, N., Bagdonas, A.F.: Local outlier factor use for the network flow anomaly detection. Secur. Commun. Netw. 8(18), 4203\u20134212 (2015)","journal-title":"Secur. Commun. Netw."},{"key":"9_CR24","doi-asserted-by":"publisher","unstructured":"Pratomo, B.A., Burnap, P., Theodorakopoulos, G.: Unsupervised approach for detecting low rate attacks on network traffic with autoencoder. In: 2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security), pp. 1\u20138 (2018). https:\/\/doi.org\/10.1109\/CyberSecPODS.2018.8560678","DOI":"10.1109\/CyberSecPODS.2018.8560678"},{"key":"9_CR25","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint. arXiv:1511.06434 (2015)"},{"key":"9_CR26","doi-asserted-by":"publisher","unstructured":"Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: SIGMOD, vol. 29, no. 2, pp. 427\u2013438 (2000). https:\/\/doi.org\/10.1145\/335191.335437","DOI":"10.1145\/335191.335437"},{"issue":"12","key":"9_CR27","first-page":"1848","volume":"2","author":"S Revathi","year":"2013","unstructured":"Revathi, S., Malathi, A.: A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. Int. J. Eng. Res. Technol. (IJERT) 2(12), 1848\u20131853 (2013)","journal-title":"Int. J. Eng. Res. Technol. (IJERT)"},{"key":"9_CR28","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1016\/j.media.2019.01.010","volume":"54","author":"T Schlegl","year":"2019","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Langs, G., Schmidt-Erfurth, U.: f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Med. Image Anal. 54, 30\u201344 (2019)","journal-title":"Med. Image Anal."},{"key":"9_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","volume-title":"Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","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: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12"},{"key":"9_CR30","doi-asserted-by":"publisher","unstructured":"Schubert, E., Koos, A., Emrich, T., Z\u00fcfle, A., Schmid, K.A., Zimek, A.: A framework for clustering uncertain data. In: Proceedings of the VLDB Endowment, vol. 8, no. 12, pp. 1976\u20131979 (2015). https:\/\/doi.org\/10.14778\/2824032.2824115","DOI":"10.14778\/2824032.2824115"},{"issue":"4","key":"9_CR31","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.3233\/IFS-130868","volume":"26","author":"A Shubair","year":"2014","unstructured":"Shubair, A., Ramadass, S., Altyeb, A.A.: kENFIS: kNN-based evolving neuro-fuzzy inference system for computer worms detection. J. Intell. Fuzzy Syst. 26(4), 1893\u20131908 (2014)","journal-title":"J. Intell. Fuzzy Syst."},{"issue":"2","key":"9_CR32","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TNSM.2021.3078381","volume":"18","author":"I Siniosoglou","year":"2021","unstructured":"Siniosoglou, I., Radoglou-Grammatikis, P., Efstathopoulos, G., Fouliras, P., Sarigiannidis, P.: A unified deep learning anomaly detection and classification approach for smart grid environments. IEEE Trans. Netw. Serv. Manage. 18(2), 1137\u20131151 (2021). https:\/\/doi.org\/10.1109\/TNSM.2021.3078381","journal-title":"IEEE Trans. Netw. Serv. Manage."},{"key":"9_CR33","doi-asserted-by":"publisher","first-page":"65579","DOI":"10.1109\/ACCESS.2019.2916648","volume":"7","author":"M Usama","year":"2019","unstructured":"Usama, M., et al.: Unsupervised machine learning for networking: techniques, applications and research challenges. IEEE Access 7, 65579\u201365615 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2916648","journal-title":"IEEE Access"},{"key":"9_CR34","doi-asserted-by":"crossref","unstructured":"Xu, H., et al.: Beyond outlier detection: interpreting outliers by attention-guided triplet deviation network. In: Proceedings of the Web Conference 2021 (WWW\u201921). ACM (2021)","DOI":"10.1145\/3442381.3449868"},{"key":"9_CR35","doi-asserted-by":"publisher","first-page":"108346","DOI":"10.1109\/ACCESS.2020.3001350","volume":"8","author":"S Zavrak","year":"2020","unstructured":"Zavrak, S., \u0130skefiyeli, M.: Anomaly-based intrusion detection from network flow features using variational autoencoder. IEEE Access 8, 108346\u2013108358 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3001350","journal-title":"IEEE Access"},{"key":"9_CR36","doi-asserted-by":"publisher","first-page":"108346","DOI":"10.1109\/ACCESS.2020.3001350","volume":"8","author":"S Zavrak","year":"2020","unstructured":"Zavrak, S., Iskefiyeli, M.: Anomaly-based intrusion detection from network flow features using variational autoencoder. IEEE Access 8, 108346\u2013108358 (2020)","journal-title":"IEEE Access"},{"key":"9_CR37","unstructured":"Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-based anomaly detection. arXiv preprint. arXiv:1802.06222 (2018)"}],"container-title":["Lecture Notes in Computer Science","Security and Trust Management"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-29504-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T10:25:52Z","timestamp":1680517552000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-29504-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031295034","9783031295041"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-29504-1_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"4 April 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"STM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Security and Trust Management","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Copenhagen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Denmark","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":"29 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"stm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sptage.compute.dtu.dk\/STM2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}