{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T11:48:52Z","timestamp":1773316132982,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":56,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819562084","type":"print"},{"value":"9789819562091","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-6209-1_21","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:29:04Z","timestamp":1767320944000},"page":"384-403","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SC-HNM: Filtering False Negatives for\u00a0Network Service Embeddings"],"prefix":"10.1007","author":[{"given":"Xuyun","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinbing","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuying","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"key":"21_CR1","unstructured":"abuse.ch: Threatfox (2025). https:\/\/threatfox.abuse.ch\/"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Acar, G., et al.: FPDetective: dusting the web for fingerprinters. In: Proceedings of the 2013 ACM SIGSAC Conference on Computer & Communications Security, pp. 1129\u20131140 (2013)","DOI":"10.1145\/2508859.2516674"},{"key":"21_CR3","unstructured":"Althouse, B.: JARM: a passive TLS fingerprinting tool for server classification (2020). https:\/\/github.com\/salesforce\/jarm"},{"key":"21_CR4","unstructured":"Benoit, T., Wang, Y., Dannehl, M., Kinder, J.: BLens: contrastive captioning of binary functions using ensemble embedding. arXiv preprint arXiv:2409.07889 (2024)"},{"key":"21_CR5","unstructured":"Cai, Y., et al.: FAMOS: robust privacy-preserving authentication on payment apps via federated multi-modal contrastive learning. In: Proceedings of the 33rd USENIX Conference on Security Symposium, pp. 289\u2013306 (2024)"},{"key":"21_CR6","unstructured":"Censys Technologies: Censys (2025). https:\/\/www.censys.com\/"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Chen, J., Xiao, S., Zhang, P., Luo, K., Lian, D., Liu, Z.: BGE M3-embedding: multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprints arXiv:2402.03216 (2024)","DOI":"10.18653\/v1\/2024.findings-acl.137"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Chen, P., Zhang, Y., Li, Z., Sun, L.: Few-shot incremental learning for label-to-image translation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3697\u20133707 (2022)","DOI":"10.1109\/CVPR52688.2022.00368"},{"key":"21_CR9","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR (2020)"},{"key":"21_CR10","unstructured":"Chen, T.S., Hung, W.C., Tseng, H.Y., Chien, S.Y., Yang, M.H.: Incremental false negative detection for contrastive learning. In: 10th International Conference on Learning Representations, ICLR 2022 (2022)"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Chen, X., He, K.: Exploring simple Siamese representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750\u201315758 (2021)","DOI":"10.1109\/CVPR46437.2021.01549"},{"key":"21_CR12","first-page":"8765","volume":"33","author":"CY Chuang","year":"2020","unstructured":"Chuang, C.Y., Robinson, J., Lin, Y.C., Torralba, A., Jegelka, S.: Debiased contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 8765\u20138775 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Chuang, Y.S., et al.: DiffCSE: difference-based contrastive learning for sentence embeddings. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4207\u20134218 (2022)","DOI":"10.18653\/v1\/2022.naacl-main.311"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"21_CR15","unstructured":"Durumeric, Z., Wustrow, E., Halderman, J.A.: $$\\{$$ZMap$$\\}$$: fast internet-wide scanning and its security applications. In: 22nd USENIX Security Symposium (USENIX Security 13), pp. 605\u2013620 (2013)"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"El-Rewini, Z., Zhang, Z., Aafer, Y.: Poirot: probabilistically recommending protections for the android framework. In: Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, pp. 937\u2013950 (2022)","DOI":"10.1145\/3548606.3560710"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., Chen, D.: SimCSE: simple contrastive learning of sentence embeddings. In: EMNLP 2021-2021 Conference on Empirical Methods in Natural Language Processing, Proceedings (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"21_CR18","doi-asserted-by":"publisher","first-page":"101873","DOI":"10.1016\/j.cose.2020.101873","volume":"95","author":"D Gibert","year":"2020","unstructured":"Gibert, D., Mateu, C., Planes, J.: Hydra: a multimodal deep learning framework for malware classification. Comput. Secur. 95, 101873 (2020)","journal-title":"Comput. Secur."},{"key":"21_CR19","first-page":"21271","volume":"33","author":"JB Grill","year":"2020","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural. Inf. Process. Syst. 33, 21271\u201321284 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Gui, J., et al.: A survey on self-supervised learning: algorithms, applications, and future trends. IEEE Trans. Pattern Anal. Mach. Intell. (2024)","DOI":"10.1109\/TPAMI.2024.3415112"},{"key":"21_CR21","doi-asserted-by":"publisher","first-page":"4015","DOI":"10.1109\/ACCESS.2021.3139835","volume":"10","author":"M Hassan","year":"2021","unstructured":"Hassan, M., Haque, M.E., Tozal, M.E., Raghavan, V., Agrawal, R.: Intrusion detection using payload embeddings. IEEE Access 10, 4015\u20134030 (2021)","journal-title":"IEEE Access"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Huynh, T., Kornblith, S., Walter, M.R., Maire, M., Khademi, M.: Boosting contrastive self-supervised learning with false negative cancellation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 2785\u20132795 (2022)","DOI":"10.1109\/WACV51458.2022.00106"},{"issue":"1","key":"21_CR24","doi-asserted-by":"publisher","first-page":"2","DOI":"10.3390\/technologies9010002","volume":"9","author":"A Jaiswal","year":"2020","unstructured":"Jaiswal, A., Babu, A.R., Zadeh, M.Z., Banerjee, D., Makedon, F.: A survey on contrastive self-supervised learning. Technologies 9(1), 2 (2020)","journal-title":"Technologies"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Jia, Y., Meng, Y., Zhuang, H.: Imcscl: image-based malware classification using self-supervised and contrastive learning. In: 2023 IEEE 23rd International Conference on Software Quality, Reliability, and Security (QRS), pp. 672\u2013683. IEEE (2023)","DOI":"10.1109\/QRS60937.2023.00071"},{"key":"21_CR26","first-page":"21798","volume":"33","author":"Y Kalantidis","year":"2020","unstructured":"Kalantidis, Y., Sariyildiz, M.B., Pion, N., Weinzaepfel, P., Larlus, D.: Hard negative mixing for contrastive learning. Adv. Neural. Inf. Process. Syst. 33, 21798\u201321809 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"21_CR27","first-page":"521","volume":"14","author":"Y Keim","year":"2022","unstructured":"Keim, Y., Mohapatra, A.: Cyber threat intelligence framework using advanced malware forensics. Int. J. Inf. Technol. 14(1), 521\u2013530 (2022)","journal-title":"Int. J. Inf. Technol."},{"key":"21_CR28","unstructured":"Li, J., Zhou, P., Xiong, C., Hoi, S.C.: Prototypical contrastive learning of unsupervised representations. arXiv preprint arXiv:2005.04966 (2020)"},{"issue":"7","key":"21_CR29","doi-asserted-by":"publisher","first-page":"2122","DOI":"10.3390\/s24072122","volume":"24","author":"L Li","year":"2024","unstructured":"Li, L., Lu, Y., Yang, G., Yan, X.: End-to-end network intrusion detection based on contrastive learning. Sensors 24(7), 2122 (2024)","journal-title":"Sensors"},{"key":"21_CR30","doi-asserted-by":"publisher","unstructured":"Li, R., Shen, M., Yu, H., Li, C., Duan, P., Zhu, L.: A survey on cyberspace search engines. In: Lu, W., et al. (eds.) CNCERT 2020. CCIS, vol. 1299, pp. 206\u2013214. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-33-4922-3_15","DOI":"10.1007\/978-981-33-4922-3_15"},{"issue":"10","key":"21_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3657282","volume":"56","author":"HI Liu","year":"2024","unstructured":"Liu, H.I., et al.: Lightweight deep learning for resource-constrained environments: a survey. ACM Comput. Surv. 56(10), 1\u201342 (2024)","journal-title":"ACM Comput. Surv."},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: HNCSE: advancing sentence embeddings via hybrid contrastive learning with hard negatives. arXiv preprint arXiv:2411.12156 (2024)","DOI":"10.1016\/j.asoc.2025.113685"},{"key":"21_CR33","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized BERT pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"issue":"1","key":"21_CR34","first-page":"5695021","volume":"2019","author":"J Lu","year":"2019","unstructured":"Lu, J., et al.: Integrating traffics with network device logs for anomaly detection. Secur. Commun. Netw. 2019(1), 5695021 (2019)","journal-title":"Secur. Commun. Netw."},{"key":"21_CR35","unstructured":"Makrushin, D.: Indicators of compromise as an instrument for threat intelligence. ResearchGate (2021)"},{"key":"21_CR36","doi-asserted-by":"crossref","unstructured":"Masukawa, R., et al.: PacketClip: multi-modal embedding of network traffic and language for cybersecurity reasoning. arXiv preprints arXiv:2503.03747 (2025)","DOI":"10.3389\/frai.2025.1593944"},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Milajerdi, S.M., Eshete, B., Gjomemo, R., Venkatakrishnan, V.: POIROT: aligning attack behavior with kernel audit records for cyber threat hunting. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pp. 1795\u20131812 (2019)","DOI":"10.1145\/3319535.3363217"},{"key":"21_CR38","doi-asserted-by":"crossref","unstructured":"Mirsky, Y., Doitshman, T., Elovici, Y., Shabtai, A.: Kitsune: an ensemble of autoencoders for online network intrusion detection. In: 25th Annual Network and Distributed System Security Symposium, NDSS 2018. The Internet Society (2018)","DOI":"10.14722\/ndss.2018.23204"},{"key":"21_CR39","doi-asserted-by":"crossref","unstructured":"Mischinger, M., Pastrana, S., Suarez-Tangil, G., et\u00a0al.: IOC stalker: early detection of indicators of compromise. In: Annual Computer Security Applications Conference (2024)","DOI":"10.1109\/ACSAC63791.2024.00074"},{"key":"21_CR40","unstructured":"Oord, A.V.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)"},{"key":"21_CR41","unstructured":"Potter, K., Stilinski, D., Adablanu, S.: Multimodal deep learning for integrated cybersecurity analytics. Technical report, EasyChair (2024)"},{"key":"21_CR42","unstructured":"Robinson, J., Chuang, C.Y., Sra, S., Jegelka, S.: Contrastive learning with hard negative samples. arXiv preprint arXiv:2010.04592 (2020)"},{"key":"21_CR43","unstructured":"Salesforce Engineering: TLS Fingerprinting with JA3 and JA3S (2017). https:\/\/engineering.salesforce.com\/tls-fingerprinting-with-ja3-and-ja3s-247362855967\/"},{"issue":"2","key":"21_CR44","first-page":"425","volume":"25","author":"S Samtani","year":"2023","unstructured":"Samtani, S., Zhao, Z., Krishnan, R.: Secure knowledge management and cybersecurity in the era of artificial intelligence. Inf. Syst. Front. 25(2), 425\u2013429 (2023)","journal-title":"Inf. Syst. Front."},{"key":"21_CR45","unstructured":"Shodan Search Engine: Shodan (2025). https:\/\/www.shodan.io\/"},{"key":"21_CR46","unstructured":"Sosnowski, M., et al.: Active TLS stack fingerprinting: characterizing TLS server deployments at scale. In: Proceedings of the Network Traffic Measurement and Analysis Conference (TMA) (2022)"},{"key":"21_CR47","doi-asserted-by":"publisher","first-page":"110299","DOI":"10.1016\/j.engappai.2025.110299","volume":"150","author":"F Wang","year":"2025","unstructured":"Wang, F., Chen, Y., Gao, H., Li, Q., Wang, C.: Self-supervised contrastive representation learning for classifying internet of things malware. Eng. Appl. Artif. Intell. 150, 110299 (2025)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"21_CR48","unstructured":"Wang, L., et al.: Text embeddings by weakly-supervised contrastive pre-training. arXiv preprint arXiv:2212.03533 (2022)"},{"key":"21_CR49","first-page":"49513","volume":"36","author":"Z Wang","year":"2023","unstructured":"Wang, Z., Mao, Y.: Sample-conditioned hypothesis stability sharpens information-theoretic generalization bounds. Adv. Neural. Inf. Process. Syst. 36, 49513\u201349541 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"4","key":"21_CR50","doi-asserted-by":"publisher","first-page":"4216","DOI":"10.1109\/TKDE.2021.3131584","volume":"35","author":"L Wu","year":"2021","unstructured":"Wu, L., Lin, H., Tan, C., Gao, Z., Li, S.Z.: Self-supervised learning on graphs: contrastive, generative, or predictive. IEEE Trans. Knowl. Data Eng. 35(4), 4216\u20134235 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"21_CR51","doi-asserted-by":"crossref","unstructured":"Wu, M., et al.: Revealing the black box of device search engine: scanning assets, strategies, and ethical consideration. arXiv preprint arXiv:2412.15696 (2024)","DOI":"10.14722\/ndss.2025.241924"},{"key":"21_CR52","unstructured":"Xu, L., et al.: Negative sampling for contrastive representation learning: a review. arXiv preprint arXiv:2206.00212 (2022)"},{"key":"21_CR53","unstructured":"Yen, T.F., Xie, Y., Yu, F., Yu, R.P., Abadi, M.: Host fingerprinting and tracking on the web: privacy and security implications. In: NDSS, vol.\u00a062, p.\u00a066 (2012)"},{"key":"21_CR54","doi-asserted-by":"crossref","unstructured":"Zhang, J., Hu, X., Jang, J., Wang, T., Gu, G., Stoecklin, M.: Hunting for invisibility: characterizing and detecting malicious web infrastructures through server visibility analysis. In: IEEE INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, pp.\u00a01\u20139. IEEE (2016)","DOI":"10.1109\/INFOCOM.2016.7524582"},{"key":"21_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/LGRS.2022.3222836","volume":"19","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Wang, X., Mei, X., Tao, C., Li, H.: False: false negative samples aware contrastive learning for semantic segmentation of high-resolution remote sensing image. IEEE Geosci. Remote Sens. Lett. 19, 1\u20135 (2022). https:\/\/doi.org\/10.1109\/LGRS.2022.3222836","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"21_CR56","unstructured":"ZoomEye: ZoomEye (2025). https:\/\/www.zoomeye.org\/"}],"container-title":["Lecture Notes in Computer Science","Information Security and Cryptology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-6209-1_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:42:47Z","timestamp":1773250967000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-6209-1_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819562084","9789819562091"],"references-count":56,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-6209-1_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Inscrypt","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Information Security and Cryptology","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xi'an","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cisc22025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/inscrypt2025.xidian.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}