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We tackle this problem in our system Janus, a log-based anomaly detection system featuring a novel Single-Pass Dual-Mask (SPDM) attention mechanism. Janus introduces a domain-specific inductive bias by partitioning attention heads into two groups. Global heads learn the valid temporal grammar of 5G procedures using a causal mask, and local heads perform fine-grained audits on the consistency of critical data fields using a tag-based semantic mask. A multi-stage curriculum learning framework progressively adapts Janus from domain pre-training to discriminative fine-tuning and learns to detect complex, real-world software failures. Experimental evaluation with several 5G log datasets demonstrates that Janus consistently outperforms prior systems, achieving on average a 3\u00d7 performance improvement over a DNN-based baseline and an 80% gain over a transformer-based system.<\/jats:p>","DOI":"10.1145\/3788096","type":"journal-article","created":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T18:49:47Z","timestamp":1774550987000},"page":"1-36","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Janus: A Dual-Mask Attention Transformer for Log-based Anomaly Detection in Cellular Networks"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9452-4504","authenticated-orcid":false,"given":"Umakant","family":"Kulkarni","sequence":"first","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2870-7166","authenticated-orcid":false,"given":"Sonia","family":"Fahmy","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,3,26]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"6G architecture. https:\/\/www.nokia.com\/6g\/6g-system-architecture-where-innovation-meets-evolution-for-a-more-sustainable-and-connected-world\/."},{"key":"e_1_2_1_2_1","unstructured":"AI-native 6G. https:\/\/www.nokia.com\/6g\/unlocking-the-full-potential-of-ai-native-6g-through-standards\/."},{"key":"e_1_2_1_3_1","unstructured":"AllReduce Operation. https:\/\/docs.nvidia.com\/deeplearning\/nccl\/user-guide\/docs\/usage\/collectives.html#allreduce."},{"key":"e_1_2_1_4_1","unstructured":"CloudLab POWER8 Server. https:\/\/www.clemson.cloudlab.us\/portal\/show-nodetype.php?type=ibm8335."},{"key":"e_1_2_1_5_1","unstructured":"CloudLab r650. https:\/\/www.clemson.cloudlab.us\/portal\/show-nodetype.php?type=r650."},{"key":"e_1_2_1_6_1","unstructured":"Common Data Types. https:\/\/www.3gpp.org\/ftp\/specs\/archive\/OpenAPI\/Rel-15\/TS29571_CommonData.yaml."},{"key":"e_1_2_1_7_1","unstructured":"Distributed Data Parallel. https:\/\/docs.pytorch.org\/tutorials\/intermediate\/ddp_tutorial.html."},{"key":"e_1_2_1_8_1","unstructured":"gnbsim. https:\/\/github.com\/omec-project\/gnbsim."},{"key":"e_1_2_1_9_1","unstructured":"Janus Dataset. https:\/\/huggingface.co\/datasets\/umakantk\/janusdata."},{"key":"e_1_2_1_10_1","unstructured":"Janus Source Code. https:\/\/github.com\/UmakantKulkarni\/Janus."},{"key":"e_1_2_1_11_1","unstructured":"K8s Log Forwarder. https:\/\/github.com\/UmakantKulkarni\/k8s-log-forwarder."},{"key":"e_1_2_1_12_1","unstructured":"Kibana. https:\/\/www.elastic.co\/kibana."},{"key":"e_1_2_1_13_1","unstructured":"KS test. https:\/\/www.itl.nist.gov\/div898\/handbook\/prc\/section2\/prc212.htm."},{"key":"e_1_2_1_14_1","unstructured":"Llama 3.2. https:\/\/www.llama.com\/docs\/model-cards-and-prompt-formats\/llama3_2\/."},{"key":"e_1_2_1_15_1","unstructured":"NVIDIA Tesla P100. https:\/\/www.nvidia.com\/en-au\/data-center\/tesla-p100\/."},{"key":"e_1_2_1_16_1","unstructured":"Open5GS. https:\/\/github.com\/open5gs\/open5gs."},{"key":"e_1_2_1_17_1","unstructured":"Open5GS Log Anomaly Generator. https:\/\/github.com\/UmakantKulkarni\/open5gs_anomaly."},{"key":"e_1_2_1_18_1","unstructured":"Telco software updates. https:\/\/techdocs.broadcom.com\/us\/en\/vmware-sde\/telco-cloud\/vmware-telco-cloud-platform\/4-0\/vmware-telco-cloud-platform-401-release-notes.html."},{"key":"e_1_2_1_19_1","volume-title":"Technical Report (TR) 23.288, 3","author":"Architecture G","year":"2021","unstructured":"3GPP. Architecture enhancements for 5G System (5GS) to support network data analytics services. Technical Report (TR) 23.288, 3 2021. Version 17.0.0."},{"key":"e_1_2_1_20_1","volume-title":"Forty-first International Conference on Machine Learning","author":"Agarwal S.","year":"2024","unstructured":"Agarwal, S., Acun, B., Hosmer, B., Elhoushi, M., Lee, Y., Venkataraman, S., Papailiopoulos, D., and Wu, C.-J. CHAI: Clustered head attention for efficient LLM inference. In Forty-first International Conference on Machine Learning (2024)."},{"key":"e_1_2_1_21_1","first-page":"2041","volume-title":"Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security (New York, NY, USA, 2024), CCS '24, Association for Computing Machinery","author":"Bennett N.","unstructured":"Bennett, N., Zhu, W., Simon, B., Kennedy, R., Enck, W., Traynor, P., and Butler, K. R. B. RANsacked: A domain-informed approach for fuzzing LTE and 5G RAN-Core interfaces. In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communications Security (New York, NY, USA, 2024), CCS '24, Association for Computing Machinery, p. 2027\u20132041."},{"key":"e_1_2_1_22_1","volume-title":"Proceedings of the International Conference on Advances in Computing, Communications and Informatics","author":"Bhuyan M. H.","year":"2012","unstructured":"Bhuyan, M. H., Bhattacharyya, D. K., and Kalita, J. K. An effective unsupervised network anomaly detection method. In Proceedings of the International Conference on Advances in Computing, Communications and Informatics (New York, NY, USA, 2012), ICACCI '12, Association for Computing Machinery, p. 533\u2013539."},{"key":"e_1_2_1_23_1","volume-title":"Telco-RAG: Navigating the challenges of retrieval-augmented language models for telecommunications. https:\/\/arxiv.org\/abs\/2404.15939","author":"Bornea A.-L.","year":"2024","unstructured":"Bornea, A.-L., Ayed, F., Domenico, A. D., Piovesan, N., and Maatouk, A. Telco-RAG: Navigating the challenges of retrieval-augmented language models for telecommunications. https:\/\/arxiv.org\/abs\/2404.15939, 2024."},{"key":"e_1_2_1_24_1","volume-title":"Do data center network metrics predict application-facing performance?","author":"Chang B.","year":"2024","unstructured":"Chang, B., Mogul, J. C., Wang, R., Zhang, M., and Akella, A. Do data center network metrics predict application-facing performance?, 2024."},{"key":"e_1_2_1_25_1","first-page":"4","article-title":"A survey of software log instrumentation","volume":"54","author":"Chen B.","year":"2021","unstructured":"Chen, B., and Jiang, Z. M. J. A survey of software log instrumentation. ACM Comput. Surv. 54, 4 (May 2021).","journal-title":"ACM Comput. Surv."},{"key":"e_1_2_1_26_1","volume-title":"Nov.","author":"D'Emmanuele V.","year":"2023","unstructured":"D'Emmanuele, V., and Raguideau, A. PacketRusher: High performance 5G UE\/gNB Simulator and CP\/UP load tester. https:\/\/github.com\/HewlettPackard\/PacketRusher, Nov. 2023."},{"key":"e_1_2_1_27_1","volume-title":"Proceedings of the 34th USENIX Conference on Security Symposium (USA, 2025), SEC '25, USENIX Association.","author":"Dong Y.","unstructured":"Dong, Y., Yang, T., Al Ishtiaq, A., Rashid, S. M. M., Ranjbar, A., Tu, K., Wu, T., Mahmud, M. S., and Hussain, S. R. Corecrisis: threat-guided and context-aware iterative learning and fuzzing of 5G core networks. In Proceedings of the 34th USENIX Conference on Security Symposium (USA, 2025), SEC '25, USENIX Association."},{"key":"e_1_2_1_28_1","first-page":"1298","volume-title":"Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (New York, NY, USA, 2017), CCS '17, Association for Computing Machinery","author":"Du M.","unstructured":"Du, M., Li, F., Zheng, G., and Srikumar, V. Deeplog: Anomaly detection and diagnosis from system logs through deep learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (New York, NY, USA, 2017), CCS '17, Association for Computing Machinery, p. 1285\u20131298."},{"key":"e_1_2_1_29_1","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1109\/INFCOM.2009.5061947","volume-title":"IEEE INFOCOM 2009","author":"Duffield N.","year":"2009","unstructured":"Duffield, N., Haffner, P., Krishnamurthy, B., and Ringberg, H. Rule-based anomaly detection on ip flows. In IEEE INFOCOM 2009 (2009), pp. 424-432."},{"key":"e_1_2_1_30_1","first-page":"1","volume-title":"Proceedings of the USENIX Annual Technical Conference (ATC) (July","author":"Duplyakin D.","year":"2019","unstructured":"Duplyakin, D., Ricci, R., Maricq, A., Wong, G., Duerig, J., Eide, E., Stoller, L., Hibler, M., Johnson, D., Webb, K., Akella, A., Wang, K., Ricart, G., Landweber, L., Elliott, C., Zink, M., Cecchet, E., Kar, S., and Mishra, P. The design and operation of CloudLab. In Proceedings of the USENIX Annual Technical Conference (ATC) (July 2019), pp. 1-14."},{"key":"e_1_2_1_31_1","volume-title":"Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)","author":"Gao J.","year":"2020","unstructured":"Gao, J., Zahran, M., Sheoran, A., Fahmy, S., and Ribeiro, B. Infinity learning: Learning Markov chains from aggregate steady-state observations. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) (2020)."},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3570361.3613256"},{"key":"e_1_2_1_33_1","first-page":"361","volume-title":"Proceedings of the ACM SIGCOMM 2011 Conference (New York, NY, USA, 2011), SIGCOMM '11, Association for Computing Machinery","author":"Gill P.","unstructured":"Gill, P., Jain, N., and Nagappan, N. Understanding network failures in data centers: measurement, analysis, and implications. In Proceedings of the ACM SIGCOMM 2011 Conference (New York, NY, USA, 2011), SIGCOMM '11, Association for Computing Machinery, p. 350\u2013361."},{"key":"e_1_2_1_34_1","volume-title":"Logllm: Log-based anomaly detection using large language models","author":"Guan W.","year":"2025","unstructured":"Guan, W., Cao, J., Qian, S., Gao, J., and Ouyang, C. Logllm: Log-based anomaly detection using large language models, 2025."},{"key":"e_1_2_1_35_1","volume-title":"Translog: A unified transformer-based framework for log anomaly detection","author":"Guo H.","year":"2023","unstructured":"Guo, H., Yang, J., Liu, J., Bai, J., Ling, Y., Li, Z., Zheng, T., Zheng, L., Hou, W., and Zhang, B. Translog: A unified transformer-based framework for log anomaly detection, 2023."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN52387.2021.9534113"},{"key":"e_1_2_1_37_1","volume-title":"netfound: Foundation model for network security. https:\/\/arxiv.org\/abs\/2310.17025","author":"Guthula S.","year":"2025","unstructured":"Guthula, S., Beltiukov, R., Battula, N., Guo, W., Gupta, A., and Monga, I. netfound: Foundation model for network security. https:\/\/arxiv.org\/abs\/2310.17025, 2025."},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCCN54977.2022.9868872"},{"key":"e_1_2_1_39_1","first-page":"1","article-title":"-Y. Toward a robust ingress for open-sourced 5G core network","author":"Hsu J.-W.","year":"2025","unstructured":"Hsu, J.-W., Jiang, X.-Y., Chen, I.-W., Chen, K.-J., Ou-Yang, C., and Huang, C.-Y. Toward a robust ingress for open-sourced 5G core network. 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In MILCOM 2024 - 2024 IEEE Military Communications Conference (MILCOM) (2024), pp. 1-6."},{"key":"e_1_2_1_45_1","volume-title":"Proceedings of the Symposium on Architectures for Networking and Communications Systems","author":"Kulkarni U.","year":"2022","unstructured":"Kulkarni, U., Sheoran, A., and Fahmy, S. The cost of stateless network functions in 5G. In Proceedings of the Symposium on Architectures for Networking and Communications Systems (New York, NY, USA, 2022), ANCS '21, Association for Computing Machinery, p. 73\u201379."},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ASE51524.2021.9678773"},{"key":"e_1_2_1_47_1","first-page":"3","article-title":"Prelog: A pre-trained model for log analytics","volume":"2","author":"Le V.-H.","year":"2024","unstructured":"Le, V.-H., and Zhang, H. Prelog: A pre-trained model for log analytics. Proc. ACM Manag. Data 2, 3 (May 2024).","journal-title":"Proc. ACM Manag. Data"},{"key":"e_1_2_1_48_1","volume-title":"PEFT: State-of-the-art parameter-efficient fine-tuning methods. https:\/\/github.com\/huggingface\/peft","author":"Mangrulkar S.","year":"2022","unstructured":"Mangrulkar, S., Gugger, S., Debut, L., Belkada, Y., Paul, S., and Bossan, B. PEFT: State-of-the-art parameter-efficient fine-tuning methods. https:\/\/github.com\/huggingface\/peft, 2022."},{"key":"e_1_2_1_49_1","first-page":"4739","volume-title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 (7 2019), International Joint Conferences on Artificial Intelligence Organization","author":"Meng W.","unstructured":"Meng, W., Liu, Y., Zhu, Y., Zhang, S., Pei, D., Liu, Y., Chen, Y., Zhang, R., Tao, S., Sun, P., and Zhou, R. Loganomaly: Unsupervised detection of sequential and quantitative anomalies in unstructured logs. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19 (7 2019), International Joint Conferences on Artificial Intelligence Organization, pp. 4739-4745."},{"key":"e_1_2_1_50_1","first-page":"224","volume-title":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA, 2016), KDD '16, Association for Computing Machinery","author":"Nandi A.","unstructured":"Nandi, A., Mandal, A., Atreja, S., Dasgupta, G. B., and Bhattacharya, S. Anomaly detection using program control flow graph mining from execution logs. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA, 2016), KDD '16, Association for Computing Machinery, p. 215\u2013224."},{"key":"e_1_2_1_51_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1145\/1824766.1824773","article-title":"Statistical anomaly detection with sensor networks","volume":"7","author":"Paschalidis I. C.","year":"2010","unstructured":"Paschalidis, I. C., and Chen, Y. Statistical anomaly detection with sensor networks. ACM Trans. Sen. Netw. 7, 2 (Sept. 2010).","journal-title":"ACM Trans. Sen. Netw."},{"key":"e_1_2_1_52_1","first-page":"5215","volume-title":"33rd USENIX Security Symposium (USENIX Security 24)","author":"Rahman M. M.","year":"2024","unstructured":"Rahman, M. M., Karim, I., and Bertino, E. CellularLint: A systematic approach to identify inconsistent behavior in cellular network specifications. In 33rd USENIX Security Symposium (USENIX Security 24) (Philadelphia, PA, Aug. 2024), USENIX Association, pp. 5215-5232."},{"key":"e_1_2_1_53_1","volume-title":"International Conference on Learning Representations","author":"Robinson J. D.","year":"2021","unstructured":"Robinson, J. D., Chuang, C.-Y., Sra, S., and Jegelka, S. Contrastive learning with hard negative samples. In International Conference on Learning Representations (2021)."},{"key":"e_1_2_1_54_1","volume-title":"Proceedings of the 26th Annual International Conference on Mobile Computing and Networking","author":"Sheoran A.","year":"2020","unstructured":"Sheoran, A., Fahmy, S., Osinski, M., Peng, C., Ribeiro, B., and Wang, J. Experience: towards automated customer issue resolution in cellular networks. In Proceedings of the 26th Annual International Conference on Mobile Computing and Networking (New York, NY, USA, 2020), MobiCom '20, Association for Computing Machinery."},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3636534.3690678"},{"key":"e_1_2_1_56_1","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/IAW.2005.1495950","volume-title":"Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop","author":"Shon T.","year":"2005","unstructured":"Shon, T., Kim, Y., Lee, C., and Moon, J. A machine learning framework for network anomaly detection using svm and ga. In Proceedings from the Sixth Annual IEEE SMC Information Assurance Workshop (2005), pp. 176-183."},{"key":"e_1_2_1_57_1","first-page":"31","volume-title":"Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement (USA, 2005), IMC '05, USENIX Association","author":"Soule A.","unstructured":"Soule, A., Salamatian, K., and Taft, N. Combining filtering and statistical methods for anomaly detection. In Proceedings of the 5th ACM SIGCOMM Conference on Internet Measurement (USA, 2005), IMC '05, USENIX Association, p. 31."},{"key":"e_1_2_1_58_1","first-page":"1","volume-title":"IEEE INFOCOM 2025 - IEEE Conference on Computer Communications","author":"Sun Y.","year":"2025","unstructured":"Sun, Y., Liu, X., Sun, Q., Wang, J., Tian, L., and Liu, J. 5GC-Fuzz: Finding deep stateful vulnerabilities in 5G core network with black-box fuzzing. In IEEE INFOCOM 2025 - IEEE Conference on Computer Communications (2025), pp. 1-10."},{"key":"e_1_2_1_59_1","first-page":"1","article-title":"Deep learning based proactive anomaly detection for 5G core control plane network function interactions","author":"Tan Y.","year":"2025","unstructured":"Tan, Y., Liu, J., Li, Y., and Wang, J. Deep learning based proactive anomaly detection for 5G core control plane network function interactions. IEEE Transactions on Cognitive Communications and Networking (2025), 1-1.","journal-title":"IEEE Transactions on Cognitive Communications and Networking ("},{"key":"e_1_2_1_60_1","first-page":"1","volume-title":"2023 IEEE\/CIC International Conference on Communications in China (ICCC)","author":"Tan Y.","year":"2023","unstructured":"Tan, Y., Wang, J., Liu, J., and Li, Y. Deep learning-based log anomaly detection for 5G core network. In 2023 IEEE\/CIC International Conference on Communications in China (ICCC) (2023), pp. 1-6."},{"key":"e_1_2_1_61_1","volume-title":"Large language model (LLM) assisted end-to-end network health management based on multi-scale semanticization","author":"Tang F.","year":"2024","unstructured":"Tang, F., Wang, X., Yuan, X., Luo, L., Zhao, M., and Kato, N. Large language model (LLM) assisted end-to-end network health management based on multi-scale semanticization, 2024."},{"key":"e_1_2_1_62_1","volume-title":"The Thirteenth International Conference on Learning Representations","author":"Tang H.","year":"2025","unstructured":"Tang, H., Lin, Y., Lin, J., Han, Q., Ke, D., Hong, S., Yao, Y., and Wang, G. Razorattention: Efficient KV cache compression through retrieval heads. In The Thirteenth International Conference on Learning Representations (2025)."},{"key":"e_1_2_1_63_1","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1109\/HPSR57248.2023.10147931","volume-title":"2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR)","author":"Tian Z.","year":"2023","unstructured":"Tian, Z., Patil, R., Gurusamy, M., and McCloud, J. ADSeq-5GCN: Anomaly detection from network traffic sequences in 5G core network control plane. In 2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR) (2023), pp. 75-82."},{"key":"e_1_2_1_64_1","volume-title":"Exploratory data analysis","author":"Tukey J. W.","year":"1977","unstructured":"Tukey, J. W., et al. Exploratory data analysis, vol. 2. Springer, 1977."},{"key":"e_1_2_1_65_1","first-page":"511","volume-title":"Proceedings of the ACM SIGCOMM 2025 Conference (New York, NY, USA, 2025), SIGCOMM '25, Association for Computing Machinery","author":"Wang C.","unstructured":"Wang, C., Zhang, X., Lu, R., Lin, X., Zeng, X., Zhang, X., An, Z., Wu, G., Gao, J., Tian, C., Chen, G., Liu, G., Liao, Y., Lin, T., Cai, D., and Zhai, E. Towards llm-based failure localization in production-scale networks. In Proceedings of the ACM SIGCOMM 2025 Conference (New York, NY, USA, 2025), SIGCOMM '25, Association for Computing Machinery, p. 496\u2013511."},{"key":"e_1_2_1_66_1","volume-title":"The Thirteenth International Conference on Learning Representations","author":"Xiao G.","year":"2025","unstructured":"Xiao, G., Tang, J., Zuo, J., junxian guo, Yang, S., Tang, H., Fu, Y., and Han, S. Duoattention: Efficient long-context LLM inference with retrieval and streaming heads. In The Thirteenth International Conference on Learning Representations (2025)."},{"key":"e_1_2_1_67_1","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/QRS57517.2022.00039","volume-title":"2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)","author":"Xie Y.","year":"2022","unstructured":"Xie, Y., Zhang, H., and Babar, M. A. Loggd: Detecting anomalies from system logs with graph neural networks. 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS) (2022), 299-310."},{"key":"e_1_2_1_68_1","first-page":"1460","volume-title":"Proceedings of the 43rd International Conference on Software Engineering (2021), ICSE '21, IEEE Press","author":"Yang L.","unstructured":"Yang, L., Chen, J., Wang, Z., Wang, W., Jiang, J., Dong, X., and Zhang, W. Semi-supervised log-based anomaly detection via probabilistic label estimation. In Proceedings of the 43rd International Conference on Software Engineering (2021), ICSE '21, IEEE Press, p. 1448\u20131460."},{"key":"e_1_2_1_69_1","first-page":"3","article-title":"Taming telecom standards with retrieval augmented generation and llms","volume":"54","author":"Yilma G. M.","year":"2025","unstructured":"Yilma, G. M., Ayala-Romero, J. A., Garcia-Saavedra, A., and Costa-Perez, X. TelecomRAG: Taming telecom standards with retrieval augmented generation and llms. SIGCOMM Comput. Commun. Rev. 54, 3 (Jan. 2025), 18\u201323.","journal-title":"SIGCOMM Comput. Commun. Rev."},{"key":"e_1_2_1_70_1","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems (Red Hook, NY, USA, 2020), NIPS '20, Curran Associates Inc.","author":"Zaheer M.","unstructured":"Zaheer, M., Guruganesh, G., Dubey, A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., and Ahmed, A. Big bird: transformers for longer sequences. In Proceedings of the 34th International Conference on Neural Information Processing Systems (Red Hook, NY, USA, 2020), NIPS '20, Curran Associates Inc."},{"key":"e_1_2_1_71_1","first-page":"1","article-title":"Logda: Dual attention-based log anomaly detection addressing data imbalance. Computers","volume":"83","author":"Zhang C.","year":"2025","unstructured":"Zhang, C., and Fu, H. Logda: Dual attention-based log anomaly detection addressing data imbalance. Computers, Materials and Continua 83, 1 (2025), 1291-1306.","journal-title":"Materials and Continua"},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2006.255127"},{"key":"e_1_2_1_73_1","first-page":"817","volume-title":"Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering","author":"Zhang X.","year":"2019","unstructured":"Zhang, X., Xu, Y., Lin, Q., Qiao, B., Zhang, H., Dang, Y., Xie, C., Yang, X., Cheng, Q., Li, Z., Chen, J., He, X., Yao, R., Lou, J.-G., Chintalapati, M., Shen, F., and Zhang, D. Robust log-based anomaly detection on unstable log data. In Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering (New York, NY, USA, 2019), ESEC\/FSE 2019, Association for Computing Machinery, p. 807\u2013817."},{"key":"e_1_2_1_74_1","volume-title":"TelecomGPT: A framework to build telecom-specfic large language models. https:\/\/arxiv.org\/abs\/2407.09424","author":"Zou H.","year":"2024","unstructured":"Zou, H., Zhao, Q., Tian, Y., Bariah, L., Bader, F., Lestable, T., and Debbah, M. TelecomGPT: A framework to build telecom-specfic large language models. https:\/\/arxiv.org\/abs\/2407.09424, 2024."}],"container-title":["Proceedings of the ACM on Measurement and Analysis of Computing Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3788096","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T18:51:54Z","timestamp":1774551114000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3788096"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,26]]},"references-count":74,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3,26]]}},"alternative-id":["10.1145\/3788096"],"URL":"https:\/\/doi.org\/10.1145\/3788096","relation":{},"ISSN":["2476-1249"],"issn-type":[{"value":"2476-1249","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,26]]},"assertion":[{"value":"2026-03-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}