[1] C. Almodovar, F. Sabrina, S. Karimi, S. Azad (2024), Log Fit: Log Anomaly Detection using Fine Tuned Language Models. IEEE Transactions on Network and Service Management.DOI: 10.1109/TNSM.2024.3358730.
[2] G. Arcas, H. Gonzales, J. Cheng (2011), Challenge 7 of the Honeynet Project Forensic Challenge 2011-Forensic analysis of a compromised server. Retrieved August, 21, 2017.
[3] X. Baril, O. Cousti´e, J. Mothe, et al., Application performance anomaly detection with LSTM on temporal irregularities in logs, Proc. 29th ACM Int. Conf. on Information & Knowledge Management, pp. 1961–1964, 2020, doi: 10.1145/3340531.341215.
[4] J. Bogatinovski, G. Madjarov, S. Nedelkoski, et al., Leveraging Log Instructions in Log-based Anomaly Detection, 2022 IEEE Int. Conf. on Services Computing (SCC), 2019, pp. 321–326. [Online]. Available: https://arxiv.org/pdf/ 2207.03206.pdf
[5] A. Brown, A. Tuor, B. Hutchinson, and N. Nichols, Recurrent neural network attention mechanisms for interpretable system log anomaly detection, in Proc. 1st Workshop on Machine Learning for Computing Systems, pp. 1–8, 2018. [Online]. Available: https://arxiv. org/pdf/1803.04967.pdf
[6] S. Bursic, V. Cuculo, and A. Amelio, Anomaly detection from log files using unsupervised deep learning, in Int. Symp. on Formal Methods, pp. 200–207, 2019, doi: 10.1007/978-3-030-54994-715.
[7] M. Catillo, A. Pecchia, and U. Villano, AutoLog: Anomaly detection by deep autoencoding of system logs, Expert Systems with Applications, 2022, doi: 10.1016/j.eswa.2021.116263.
[8] R. Chalapathy and S. Chawla, Deep learning for anomaly detection: A survey, arXiv preprint arXiv:1901.03407, 2019.
[9] V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection: A survey, ACM Computing Surveys, vol. 41, no. 3, pp. 1–58, 2009. [Online]. Available: https://conservancy.umn. edu/bitstream/handle/11299/215731/07017.pdf?sequence=1
[10] Y. Chang, N. Luktarhan, J. Liu, and Q. Chen, ETCNLog: A System Log Anomaly Detection Method Based on Efficient Channel Attention and Temporal Convolutional Network, Electronics, vol. 12, no. 8, 2023, doi: 10.3390/electronics1208187.
[11] P. Cheansunan and P. Phunchongharn, Detecting anomalous events on distributed systems using convolutional neural networks, in 10th Int. Conf. on Awareness Science and Technology, pp. 1–5, 2019, doi: 10.1109/ICAwST.2019.8923357.
[12] R. Chen, S. Zhang, D. Li, et al., Log transfer: Cross-system log anomaly detection for software systems with transfer learning, in IEEE 31st Int. Symp. on Software Reliability Engineering, pp. 37–47, 2020, doi: 10.1109/ISSRE5003.2020.00013.
[13] S. Chen and H. Liao, Bert-log: Anomaly detection for system logs based on a pre-trained language model, Applied Artificial Intelligence, 2022, doi: 10.1080/08839514.2022.2145642.
[14] Z. Chen, J. Liu, W. Gu, Y. Su, and M. R. Lyu, Experience report: Deep learning-based system log analysis for anomaly detection, 2021, doi: 10.1145/1122445.1122456.
[15] L. Chen, C. Song, X. Wang, D. Fu, and F. Li, CSCLog: A Component Subsequence Correlation-Aware Log Anomaly Detection Method, arXiv preprint arXiv:2307.03359, 2023.
[16] A. Chuvakin, Public security log-sharing site, 2010. [Online]. Available:
https://logsharing.dreamhosters.
[17] Y. Cui, Y. Sun, J. Hu, et al., A convolutional auto-encoder method for anomaly detection on system logs, in Proc. IEEE SMC, pp. 3057–3062, 2018, doi: 10.1109/SMC.2018.00519.
[18] L. Decker , D. Leite , F. Viola F, et al. (2020), Comparison of evolving granular classifiers applied to anomaly detection for predictive maintenance in computing centres. IEEE Conference on evolving and adaptive Intelligence, DOI: 10.1109/EAIS48028.2020.9122779.
[19] J. Devlin, M. W. Chang, K. Lee, and K. Toutanova, Bert: Pre-training of deep bidirectional transformers for language understanding, arXiv:1810.04805, 2018, doi: 10.48550/arXiv.1810.04805.
[20] M. Du, F. Li, G. Zheng, et al., ”Deeplog: Anomaly detection and diagnosis from system logs through deep learning, in ACM SIGSAC Conf. on Computer and Communications Security, pp. 1285–1298, 2017, doi: 10.1145/3133956.3134015.
[21] Q. Du, L. Zhao, J. Xu, et al., Log-based anomaly detection with a multi-head scaled dot-product attention mechanism, in Int. Conf. on Database and Expert Systems, 2021, doi: 10.1007/978-3-030-86472-9-31.
[22] C. Eoghan and G. R. I. Golden, URL: http://old.dfrws.org/2009/challenge/ index.shtml, 2009.
[23] Y. Fang , Z. Zhao , Y. Xu , et al. (2023), Log Anomaly Detection Based on Hierarchical Graph Neural Network and Label Contrastive Coding. Computers, Materials & Continua 2023(2):74–74, 10.32604/cmc.2023.033124.
[24] A. Farzad, Log message anomaly detection with oversampling, Int. J. Artificial Intelligence and Applications (IJAIA), vol. 4, pp. 11–11, 2020, doi: 10.5121/ijaia.2020.11405.
[25] A. Farzad and T. Gulliver, A Log message anomaly detection with fuzzy C-means and MLP, Applied Intelligence, 2022, pp. 17708–17717, doi: 10.1007/s10489-022-03300-1.
[26] A. Farzad and T. Gulliver, Log message anomaly detection and classification using auto-B/LSTM and auto-GRU, arXiv preprint arXiv:1911.0874, 2019.
[27] A. Farzad and T. A. Gulliver, Two class pruned log message anomaly detection, SN Computer Science, vol. 2, no. 5, pp. 1–18, 2021, doi: 10.1007/s42979-021-00772-9.
[28] G. Li, J. Mo, G. Zhou, and C. Li, HiparaLog: Improving Log-based Anomaly Detection through Parameter Feature Integration, in 2024 Int. Joint Conf. on Neural Networks (IJCNN), Yokohama, Japan, pp. 1–8, 2024, doi: 10.1109/IJCNN60899.2024.10650376.
[29] S. Garfinkel, P. Farrell, V. Roussev, et al., Bringing science to digital forensics with standardised forensic corpora, Digital Investigation, vol. 6, pp. 2–11, 2009.
[30] I. Giurgiu and Crosby, URL: https://github. com/microservices-demo, 2017.
[31] O. Gorokhov, M. Petrovskiy, I. Mashechkin, et al., Fuzzy CNN Autoencoder for Unsupervised Anomaly Detection in Log Data, Mathematics, vol. 18, 3995, 2023, doi:10.3390/math11183995.
[32] O. Gorokhov, M. Petrovskiy, I. Mashechkin, Convolutional neural networks for unsupervised anomaly detection in text data, in Int. Conf. on Intelligent Data Engineering and Automated Learning, pp. 500–507, 2017, doi:10.1007/9783-319-68935-7-54.
[33] A. Grover, Anomaly detection for application log data, 2018, doi:10.31979/etd.znsb-bw.
[34] S. Gu, Y. Chu, W. Zhang, et al., Research on system log anomaly detection combining two-way slice GRU and GA-attention mechanism, in 4th Int. Conf. on Artificial Intelligence and Big Data, pp. 577–583, 2021, doi:10.1109/ICAIBD51990.2021.9459087.
[35] H. Guo, X. Lin, J. Yang, et al., Translog: A unified transformer-based framework for log anomaly detection, CoRR, 2022, doi:10.48550/arXiv.2201.00016.
[36] H. Guo, S. Yuan, X. Wu, Logbert: Log anomaly detection via Bert, in Int. Joint Conf. on Neural Networks, pp. 1–8, 2021, doi:10.1109/IJCNN52387.2021.9534113.
[37] H. Guo , Y. Guo , J. Yang , et al. (2023), LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction. In: International Conference on Database Systems for Advanced Applications. Springer, pp 490–501, doi:10.1007/978-3-031-30678-5-36.
[38] H. Guo, J. Liu, W. Gu, Y. Su, M. R. Lyu, LogFormer: A Pre-train and Tuning Pipeline for Log Anomaly Detection, 2024, doi:10.48550/arXiv.2401.04749.
[39] Y. Guo , Y. Wu , Y. Zhu , et al. (2021), Anomaly detection using distributed log data: A lightweight federated learning approach. 2021 international joint conference on neural networks 2021, doi:10.1109/IJCNN52387.2021.9533294.
[40] D. Han, M. Sun, M. Li, et al.,(2023), LTAnomaly: A Transformer Variant for Syslog Anomaly Detection Based on MultiScale Representation and Long Sequence Capture, Applied Sciences, vol. 13, 2023, doi:10.3390/app13137668.
[41] N. Han, S. Lu, D. Wang, et al., Skdlog: self-knowledge distillation-based CNN for abnormal log detection, in 19th IEEE Int. Conf. on Ubiquitous Intelligence and Computing, 2022, doi:10.1109/SmartWorldUIC-ATC-ScalComDigitalTwinPriCompMetaverse56740.2022.00122.
[42] X. Han , S. Yuan , M. Trabelsi , et al. (2023), Log Anomaly Detection via GPT. 2023 IEEE International Conference on Big Data (BigData) pp 2023–2023, doi: 10.1109/BigData59044.2023.10386543.
[43] X. Han , S. Yuan , LogTAD (2021), Unsupervised cross-system log anomaly detection via domain adaptation. Proceedings of the 30th ACM international conference on information & knowledge management pp 3068–3072, doi:10.1145/3459637.3482209.
[44] M. Hariharan, A. Mishra, S. Ravi, A. Sharma, A. Tanwar, K. Sundaresan, R. Karthik, (2023), Detecting log anomaly using subword attention encoder and probabilistic feature selection. Applied Intelligence, 1-16.
[45] S. Hashemi, M. Ma¨ntyla¨, OneLog: Towards endto-end training in software log anomaly detection, 2021, doi:10.48550/arXiv.2104.07324.
[46] S. Hashemi, M. M¨antyl¨a, SiaLog: detecting anomalies in software execution logs using the siamese network, Automated Software Engineering, vol. 29, no. 2, pp. 61–61, 2022, doi:10.1007/s10515-022-00365-7.
[47] S. He, T. Deng , B. Chen, et al. (2023), Unsupervised Log Anomaly Detection Method Based on Multi-Feature. Computers, Materials & Continua 2023(1):76–76,
https://doi.org/10.48550/arXiv.2008.06448.
[48] S. He, J. Zhu, P. He, M. R. Lyu, Loghub: An extensive collection of system log datasets towards automated log analytics, 2020, doi:10.48550/arXiv.2008.06448.
[49] S. He, J. Zhu, P. He, et al., Experience report: System log analysis for anomaly detection, in 2016 IEEE 27th Int. Symp. on Software Reliability Engineering (ISSRE), pp. 207–218, 2016, doi:10.1109/ISSRE.2016.21.
[50] Z. He, Y. Tang, K. Zhao, J. Liu and W.c. Chen, Graph-Based Log Anomaly Detection via Adversarial Training. Dependable Software Engineering. Theories, Tools, and Applications. SETTA 2023, 14464
https://doi.org/10.1007/978-981-99-8664-4-4.
[51] R. Hirakawa, K. Tominaga, Y. Nakatoh, Software log anomaly detection through one transformer encoder representation clustering class, in Int. Conf. on Human-Computer Interaction, 2020.
[52] S. Huang, Y. Liu, C. Fung, et al., Hitanomaly: Hierarchical transformers for anomaly detection in the system log, IEEE Trans. on Network and Service Management, vol. 17, no. 4, pp. 2064– 2076, 2020, doi:10.1109/TNSM.2020.3034647.
[53] A. Iqbal, R. Amin, F. S. Alsubaei, A. Alzahrani, Anomaly detection in multivariate time series data using deep ensemble models, PLoS ONE, vol. 19, no. 6, e0303890, 2024, doi:10.1371/journal.pone.0303890.
[54] T. Jia, Y. Li, Y. Yang, et al., Augmenting Log-based Anomaly Detection Models to Reduce False Anomalies with Human Feedback, in 28th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining, pp. 3081–3089, 2022, doi:10.1145/3534678.3539106.
[55] L. S. Ramos Ju´nior, D. Macˆedo, A. L. Oliveira, C. Zanchettin, (2022), Detecting Malicious HTTP Requests Without Log Parser Using RequestBERT-BiLSTM. In Brazilian Conference on Intelligent Systems (pp. 328-342). Cham: Springer International Publishing.Kan D, Fang X (2023) ,doi:10.1007/s00530-02301199-3.
[56] Y. Kawachi, Y. Koizumi, N. Harada, Complementary set variational autoencoder for supervised anomaly detection, in 2018 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 2366–2370, 2018, doi:10.1109/ICASSP.2018.8462181.
[57] S. Kong, J. Ai, M. Lu, et al., GRAND: GAN-based software runtime anomaly detection method using trace information, Neural Networks, vol. 169, pp. 365–377, 2024, doi:10.1016/j.neunet.2023.10.036.
[58] L. G. Korzeniowski, K. Goczyla, Landscape of automated log analysis: a systematic literature review and mapping study, IEEE Access, vol. 10, pp. 21892–21913, 2022, doi:10.1109/ACCESS.2022.3152549.
[59] C. Kruegel, G. Vigna, Anomaly detection of web-based attacks, in 10th ACM Conf. on Computer and Communications Security, pp. 251– 261, 2003, doi:10.1145/948109.948144.
[60] D. Kwon, H. Kim, J.Kim, S. C. Suh, I. Kim, , and K. J. Kim, A survey of deep learningbased network anomaly detection, Cluster Computing, vol. 22, no. 1, pp. 949–961, 2019. doi: 10.1145/948109.948144
[61] M. Landauer , S. Onder , F. Skopik , et al., Deep learning for anomaly detection in log data: A survey, Machine Learning with Applications, 2023. doi: 10.1016/j.mlwa.2023.100470
[62] M. Landauer , F. Skopik , M. Wurzenberger , et al., Have it your way: Generating customised log datasets with a model-driven simulation testbed, IEEE Transactions on Reliability, 2021. doi: 10.1109/TR.2020.303131
[63] M. Landauer , M.Wurzenberger , F. Skopik , et al., Dynamic log file analysis: An unsupervised cluster evolution approach for anomaly detection, Computers & Security, vol. 79, pp. 94–116, 2018. doi: 10.1016/j.cose.2018.08.009
[64] M. Landauer , F.Skopik , M.Wurzenberger , et al., System log clustering approaches for cyber security applications: A survey, Computers & Security, 2020, pp. 92–92. doi: 10.1016/j.cose.2020.101739
[65] VH. Le , H. Zhang , Log-based anomaly detection with deep learning: How far are we? Proceedings of the 44th International Conference on Software Engineering, pp. 1356–1367, 2022. doi: 10.1145/3510003.3510155
[66] VH. Le , H. Zhang , Neural log. Log-based anomaly detection without log parsing, 2021 36th IEEE/ACM International Conference on Automated Software Engineering, pp. 492–504. doi: 10.1109/ASE51524.2021.9678773
[67] Y. Lecun , Y. Bengio , G. Hinton , Deep learning, Nature, vol. 521, no. 7553, pp. 436–444, 2015. doi: 10.1038/nature14539
[68] Y. Lee, J. Kim, P. Kang, P., and Lanobert, 2023, System log anomaly detection based on the Bert masked language model, Applied Soft Computing, vol. 146, pp. 110689. doi: 10.1016/j.asoc.2023.110689
[69] H. Li, and Y. Li, (2020), Logspy: System log anomaly detection for distributed systems, International Conference on Artificial Intelligence and Computer Engineering, pages 347–352, doi: 10.1109/ICAICE51518.2020.00073.
[70] X. Li , P. Chen , L. Jing , et al. (2020), Swisslog: Robust and unified deep learning-based log anomaly detection for diverse faults, IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) pp 92–103, doi: 10.1109/ISSRE5003.2020.00018.
[71] X. Li , P. Chen , L. Jing , et al., Swiss Log: Robust anomaly detection and localisation for interleaved unstructured logs,” IEEE Transactions on Dependable and Secure Computing, 2022, doi: 10.1109/TDSC.2022.3162857
[72] Y. Li, Y. Liu, H. Wang, Z.Chen, W. Cheng, Y. Chen, ... & C. Liu, Glad: Content-aware dynamic graphs for log anomaly detection. In 2023 IEEE International Conference on Knowledge Graph (ICKG) (pp. 9-18). IEEE, doi: 10.1109/ICKG59574.2023.0.
[73] Z. Li, J. Shi, M. van Leeuwen, (2023), Graph Neural Network-based Log Anomaly Detection and Explanation, doi:10.48550/arXiv.2307.00527.
[74] Li, Zhong, J. Shi, and M. Van Leeuwen, Graph Neural Networks based Log Anomaly Detection and Explanation, Proceedings of the 2024 IEEE/ACM 46th International Conference on Software Engineering: Companion Proceedings, 2024.
[75] HJ. Liao , CHR. Lin , YC. Lin , et al., Intrusion detection system: A comprehensive review, Journal of Network and Computer Applications, vol. 36, no. 1, pp. 16–24, 2013. doi: 10.1016/j.jnca.2012.09.00.
[76] L. Liao , K. Zhu , J. Luo , et al., Log Anomaly Detection Based on System Behavior Analysis and Global Semantic Awareness, International Journal of Intelligent Systems, 2023. doi: 10.1155/2023/3777826.
[77] Q. Lin , H. Zhang , JG. Lou , et al., Log clustering-based problem identification for online service systems, Proceedings of the 38th International Conference on Software Engineering Companion, pp. 102–111, 2016. doi: 10.1145/2889160.2889232.
[78] C. Liu, M. Liang, J. Hou, J. Gu, Z.Wang, (2022), LogCAD: An Efficient and Robust Model for Log-Based Conformal Anomaly Detection. Security and Communication Networks, 2022, doi:10.1155/2022/5822124.
[79] FT. Liu , KM. Ting , ZH.Zhou , Isolation forest, Eighth IEEE International Conference on Data Mining, pp. 413–422, 2008. doi: 10.1109/ICDM.2008.17
[80] X. Liu , W. Liu , X. Di , et al. (2021), LogNADS: Network anomaly detection scheme based on log semantics representation Future Generation Computer Systems 124:390–405, doi:10.1016/j.future.2021.05.024.
[81] J. G. Lou, Q. Fu, S.Yang, Y. Xu, & J. Li, Mining invariants from console logs for system problem detection, In 2010 USENIX Annual Technical Conference (USENIX ATC 10), 2010. [Online]. Available: https://www.usenix.org/legacy/ event/atc10/tech/full_papers/Lou.pdf
[82] S. Lu, N. Han, M. Wang, X. Wei, Z. Lin, and D. Wang, SSDLog: a semi-supervised dual branch model for log anomaly detection, World Wide Web, pp. 1–17, 2023. doi: 10.1007/s11280-02301174-y.
[83] S. Lu, X. Wei, Y. Li, and L. Wang, Detecting anomalies in big data system logs using convolutional neural network. 2018 IEEE 16th Intl Conf on dependable, autonomic and secure computing, 16th Intl Conf on pervasive intelligence and computing, 4th Intl Conf on considerable data intelligence and computing and Cyber Science and Technology Congress, pages 151–158, doi: 10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.000.
[84] D. Lv , N. Luktarhan , Y. Chen , ConAnomaly: Content-based anomaly detection for system logs, Sensors, vol. 21, no. 18, pp. 6125, 2021. doi: 10.3390/s2118612
[85] R. Marty, A. Chuvakin, & S. Tricaud, The Honeynet Project 2010 challenge 5 – log mysteries, 2022. [Online]. Available: https://www.honeynet.org/challenges/ forensic-challenge-7-analysisof-acompromised-server/
[86] W. Meng, Y. Liu, Y. Zhu, S. Zhang, D. Pei, and Y. Liu, Unsupervised detection of sequential and quantitative anomalies in unstructured logs, IJCAI, pp. 4739–4745, 2019. [Online]. Available:
https://nkcs.iops.ai/wpcontent/uploads/2019/06/paper-IJCAI19LogAnomaly.pdf
[87] M. Munir, S. A. Siddiqui, A. Dengel, & S. Ahmed, DeepAnT: A deep learning approach for unsupervised anomaly detection in time series, IEEE Access, vol. 7, pp. 1991–2005, 2018. doi: 10.1109/ACCESS.2018.2886457
[88] S. Nedelkoski, J. Bogatinovski, A. Acker, J. Cardoso, O. Kao, and Logsy, Self-attentive classification-based anomaly detection in unstructured logs, 2020 IEEE International Conference on Data Mining, pp. 1196–1201. doi: 10.1109/ICDM50108.2020.001
[89] G. No, Y. Lee, H. Kang, & P. Kang, RAPID: Training-free Retrieval-based Log Anomaly Detection with PLM considering Token-level information, 2023. doi: 10.48550/arXiv.2311.0516
[90] A. Oliner and J. Stearley, What supercomputers say: A study of five system logs, in 37th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, 2007, pp. 575–584, doi:10.1109/DSN.2007.103.
[91] K. Otomo, S. Kobayashi, K. Fukuda, and H. Esaki, Finding anomalies in network system logs with latent variables, Proc. Workshop Big Data Anal, 2018, pp. 8–14, doi:10.1145/3229607.3229608.
[92] K. Otomo, S. Kobayashi, K. Fukuda, and H. Esaki, Latent variable-based anomaly detection in network system logs, IEICE Transactions on Information and Systems, vol. 102, no. 9, pp. 1644–1652, 2019, doi:10.1587/transinf.2018OFP0007.
[93] A. Patil, A. Wadekar, T. Gupta, R. Vijan, and F. Kazi, Explainable LSTM model for anomaly detection in HDFS log file using layerwise relevance propagation, IEEE Bombay Section Signature Conference, 2019, pp. 1–6, doi:10.1109/IBSSC47189.2019.8973044.
[94] J. Qi, Z. Luan, S. Huang, C. Fung, H. Yang, and D. Qian, LogEncoder: Logbased Contrastive Representation Learning for anomaly detection, IEEE Transactions on Network and Service Management, 2023, doi:10.1109/TNSM.2023.3239522.
[95] J. Qi, Z. Luan, S. Huang, C. Fung, H. Yang, and D. Qian, SpikeLog: Log-based anomaly detection via Potential-assisted Spiking Neuron Network, IEEE Transactions on Knowledge and Data Engineering, 2023, doi:10.1109/TKDE.2023.3347695.
[96] J. Qi, Z. Luan, S. Huang, Y. Wang, C. Fung, H. Yang, and D. Qian, Ad anomaly: adaptive anomaly detection for system logs with adversarial learning, NOMS 2022- 2022 IEEE/IFIP Network Operations and Management Symposium, 2022, pp. 1–5, doi:10.1109/NOMS54207.2022.9789917.
[97] R. Xu and Y. Li, Interpretable Spatial–Temporal Graph Convolutional Network for System Log Anomaly Detection, Advanced Engineering Informatics, vol. 62, Part C, 102803, 2024, doi:10.1016/j.aei.2024.102803.
[98] B. Scholkopf, J. C. Platt, J. Shawe-Taylor, et al., Estimating the support of a highdimensional distribution, Neural Computation, vol. 13, no. 7, pp. 1443–1471, 2001, doi:10.1162/089976601750264965.
[99] E. Serkani, H. G. Garakani, and N. Mohammadzadeh, Anomaly detection uses SVM as a classifier and decision tree to optimise feature vectors, The ISC International Journal of Information Security, 2019, doi:10.22042/isecure.2019.164980.448.
[100] R. Sinha, R. Sur, and R. Sharma, Anomaly Detection Using System Logs: A Deep Learning Approach, International Journal of Information Security and Privacy (IJISP), vol. 2022, no. 1, doi:10.4018/IJISP.285584.
[101] H. Song, Z. Jiang, A. Men, and B. Yang, A hybrid semi-supervised anomaly detection model for high-dimensional data, Computational Intelligence and Neuroscience, 2017, doi:10.1155/2017/8501683.
[102] H. Studiawan and F. Sohel, Anomaly detection in a forensic timeline with deep autoencoders, Journal of Information Security and Applications, vol. 63, 103002, 2021, doi:10.1016/j.jisa.2021.103002.
[103] H. Studiawan, F. Sohel, C. Payne, et al., Anomaly detection in operating system logs with deep learning-based sentiment analysis, IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 5, 2020, doi:10.1109/TDSC.2020.3037903.
[104] L. Sun and X. Xu, LogPal: A Generic Anomaly Detection Scheme of Heterogeneous Logs for Network Systems, Security and Communication Networks, 2023, doi:10.1155/2023/2803139.
[105] P. Sun, E. Yuepeng, T. Li, et al., Contextaware learning for anomaly detection with imbalanced log data, 2020 IEEE 22nd International Conference on High Performance Computing and Communications, 2020, pp. 449–456, doi:10.1109/HPCC-SmartCityDSS50907.2020.00055.
[106] T. Sundqvist, M. H. Bhuyan, J. Forsman, et al., Boosted ensemble learning for anomaly detection in 5G RAN, IFIP International Conference on Artificial Intelligence Applications and Innovations, 2020, pp. 15–30, doi:10.1007/9783-030-49161-1-2.
[107] T. Sutthipanyo, T. Lamsan, W. Thawornsusin, et al., Log-Based Anomaly Detection Using CNN Model with Parameter Entity Labeling for Improving Log Preprocessing Approach, TENCON 2023, pp. 914–919, doi:10.1109/TENCON58879.2023.10322478.
[108] S. Syngal, S. Verma, K. Karthik, et al., Serverlanguage processing: A semi-supervised approach to server failure detection, 2021 2nd International Conference on Computing, Networks and the Internet of Things, pp. 1–7, doi:10.1109/TKDE.2023.3347695.
[109] D. M. Tax and R. P. Duin, Support vector data description, Machine Learning, vol. 54, pp. 45–66, 2004, doi:10.1023/B:MACH.0000008084.60811.49.
[110] G. Tian, N. Luktarhan, H. Wu, et al., CLDTLog: System Log Anomaly Detection Method Based on Contrastive Learning and Dual Objective Tasks, Sensors, vol. 23, no. 11, 5042, 2023, doi:10.3390/s23115042.
[111] A. Tuor, S. Kaplan, B. Hutchinson, N. Nichols, S. Robinson, Y. Xie, H. Zhang, and M. A. Babar, LogGD: Detecting Anomalies from System Logs with Graph Neural Networks, in 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), pp. 299–310, 2022, doi:10.1109/QRS57517.2022.00039.
[112] A. Wadekar, T. Gupta, R. Vijan, et al., Hybrid CAE-VAE for unsupervised anomaly detection in log file systems, 2019 10th International Conference on Computing, Communication, and Networking Technologies, pp. 1–7, 2019, doi:10.1109/ICCCNT45670.2019.8944863.
[113] Y. Wan, Y. Liu, D. Wang, et al., GLADPAW: Graph-based log anomaly detection by position-aware weighted graph attention network, Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 66–77, 2021, doi:10.1007/978-3-030-75762-5-6.
[114] H. Wang , Y. Chen , C. Zhang , et al. (2022), GenGLAD: A Generated Graph-Based Log Anomaly Detection Framework. In: International Conference on Smart Computing and Communication. Springer Nature Switzerland, pp 11–22, doi:10.1007/978-3031-28124-2-2.
[115] J. Wang , C. Zhao , S. He , et al. (2022), LogUAD: log unsupervised anomaly detection based on Word2Vec. Computer Systems Science and Engineering 2022, doi:10.32604/csse.2022.022365.
[116] M. Wang, L. Xu, and L. Guo, Anomaly detection of system logs based on natural language processing and deep learning, in 2018 4th International Conference on Frontiers of Signal Processing (ICFSP), pp. 140–144, 2018, doi:10.1109/ICFSP.2018.8552075.
[117] Q. Wang, X. Zhang, X. Wang, et al., Log sequence anomaly detection method based on contrastive adversarial training and dual feature extraction, Entropy, vol. 1, 24, 2021, doi:10.3390/e24010069.
[118] X. Wang , Q. Cao , Q. Wang , et al. (2022), Robust log anomaly detection based on contrastive learning and multi-scale MASS. J Supercomputer 2022:17491–17512, doi:10.1007/s11227022-04508-1.
[119] X. Wang, L. Yang, D. Li, et al., MADDC: MultiScale Anomaly Detection, Diagnosis and Correction for Discrete Event Logs, in Proceedings of the 38th Annual Computer Security Applications Conference, 2022, pp. 769–784, doi:10.1145/3564625.3567972.
[120] Z. Wang , Z. Chen , J. Ni , et al. (2021), Multiscale one-class recurrent neural networks for discrete event sequence anomaly detection. Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining pp 3726–3734, doi:10.1145/3447548.3467125.
[121] Z. Wang , J. Tian , H. Fang , et al. (2022), LightLog: A lightweight temporal convolutional network for log anomaly detection on edge. Computer Networks 203, doi: 10.1016/j.comnet.2021.108616.
[122] S. Wang, et al., Deep learning-based anomaly detection and log analysis for computer networks, arXiv preprint arXiv:2407.05639, 2024.
[123] S. R. Wibisono and A. I. Kistijantoro, Log anomaly detection using an adaptive universal transformer, in 2019 International Conference of Advanced Informatics: Concepts, Theory, and Applications, pp. 1–6, 2019, doi:10.1109/ICAICTA.2019.8904299.
[124] T. Wittkopp, A. Acker, S. Nedelkoski, J. Bogatinovski, D. Scheinert, W. Fan, and O. Kao, A2log: attentive augmented log anomaly detection, arXiv preprint arXiv:2109.09537, 2021, doi:10.48550/arXiv.2109.09537.
[125] L. Xi, Y. Xin, S. Luo, et al., Anomaly detection mechanism based on hierarchical weights through large-scale log data, in 2021 International Conference on Computer Communication and Artificial Intelligence, pp. 106–115, 2021, doi:10.1109/CCAI50917.2021.9447458.
[126] B. Xia, Y. Bai, J. Yin, Y. Li, and J. Xu, LogGAN: A log-level generative adversarial network for anomaly detection using permutation event modelling, 2021, doi:10.1007/s10796-02010026-3.
[127] C. Xiao, J. Huang, and W. Wu, Detecting anomalies in cluster system using a hybrid deep learning model, International Symposium on Parallel Architectures, Algorithms and Programming, pp. 393–404, 2019, doi:10.1007/978981-15-2767-8-35.
[128] Y. Xie, L. Ji, X. Cheng, et al., An attentionbased GRU network for anomaly detection from system logs, IEICE Transactions on Information and Systems, 2020(8):1916–1919, 2020, doi:10.1587/transinf.2020EDL8016.
[129] Y. Xie, H. Zhang, and M. A. Babar, LogGD: Detecting Anomalies from System Logs with Graph Neural Networks, in 2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS), pp. 299–310, 2022, doi:10.1109/QRS57517.2022.00039.
[130] Y. Xie, H. Zhang, and M. A. Babar, LogSD: Detecting Anomalies from System Logs through Self-Supervised Learning and Frequency-Based Masking, Proceedings of the ACM on Software Engineering, vol. 1, FSE, pp. 2098–2120, 2024.
[131] W. Xu, L. Huang, A. Fox, et al., Large-scale system problem detection by mining console logs, Proceedings of SOSP’09, 2009.
[132] W. Xu, L. Huang, A. Fox, et al., Detecting largescale system problems by mining console logs, Proceedings of the ACM SIGOPS 22nd Symposium on Operating Systems Principles, pp. 117–132, 2009, doi:10.1145/1629575.1629587.
[133] P. Xu, et al., Multi-source data based anomaly detection through temporal and spatial characteristics, Expert Systems with Applications, vol. 237, 121675, 2024.
[134] R. B. Yadav, P. S. Kumar, and S. V. Dhavale, A survey on log anomaly detection using deep learning, in 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), pp. 1215–1220, 2020, doi:10.1109/ICRITO48877.2020.9197818.
[135] L. Yan, C. Luo, and R. Shao, Discrete log anomaly detection: A novel time-aware graph-based link prediction approach, Information Sciences, vol. 647, 119576, 2023, doi:10.1016/j.ins.2023.119576.
[136] L. Yang, J. Chen, Z. Wang, et al., Semisupervised log-based anomaly detection via probabilistic label estimation, in 2021 IEEE/ACM 43rd International Conference on Software Engineering, pp. 1448–1460, 2021, doi:10.1109/ICSE43902.2021.00130.
[137] R. Yang, D. Qu, Y. Gao, et al., NLSALog: An anomaly detection framework for log sequence in security management, IEEE Access, vol. 7, pp. 181152–181164, 2019, doi:10.48550/arXiv.1710.00811.
[138] S. Yen, M. Moh, T. S. Moh, et al., Semisupervised log anomaly detection through sequence modelling, 18th IEEE International Conference on Machine Learning and Applications, pp. 1334–1341, 2019, doi:10.1109/ICMLA.2019.00217.
[139] K. Yin, M. Yan, L. Xu, et al., Improving log-based anomaly detection with component-aware analysis, 2020 IEEE International Conference on Software Maintenance and Evolution, pp. 667–671, 2020, doi:10.1109/ICSME46990.2020.00069.
[140] F. Yuan, Y. Cao, Y. Shang, J. Tan, and B. Fang, Insider threat detection with deep neural network,” in International Conference on Computational Science, pp. 43–54, 2018, doi:10.1007/9783-319-93698-7-4.
[141] W. Yuan, S. Ying, X. Duan, et al., PVE: A log parsing method based on VAE using embedding vectors, Information Processing & Management, vol. 60, no. 5, 2023, doi:10.1016/j.ipm.2023.103476.
[142] C. Zhang, X. Wang, H. Zhang, et al., Log sequence anomaly detection based on local information extraction and globally sparse transformer model, IEEE Transactions on Network and Service Management, vol. 18, no. 4, 2021, doi:10.1109/TNSM.2021.3125967.
[143] C. Zhang, X. Peng, C. Sha, K. Zhang, Z. Fu, X. Wu, ... & D. Zhang, (2022), Deeptralog: Tracelog combined microservice anomaly detection through graph-based deep learning. In Proceedings of the 44th international conference on software engineering (pp. 623-634).
[144] D. Zhang, Y. Zheng, Y. Wen, et al., Role-based log analysis applying deep learning for insider threat detection, in Proceedings of the 1st Workshop on Security-Oriented Designs of Computer Architectures and Processors, pp. 18–20, 2018, doi:10.1145/3267494.3267495.
[145] D. Zhang, D. Dai, R. Han, et al., SentiLog: Anomaly detecting on parallel file systems via log-based sentiment analysis, in Proceedings of the 13th ACM Workshop, pp. 86–93, 2021, doi:10.1145/3465332.3470873.
[146] K. Zhang , X. Di , X. Liu , et al. (2022), LogLR A Log Anomaly Detection Method Based on Logical Reasoning. International Conference on Wireless Algorithms, Systems, and Applications 2022 pp 489–500, httpdoi:10.1007/978-3-03119214-2-41.
[147] L. Zhang, W. Li, Z. Zhang, et al., LogAttn: Unsupervised log anomaly detection with an AutoEncoder-based attention mechanism, in International Conference on Knowledge Science, Engineering and Management, pp. 222–235, 2021, doi:10.1007/978-3-030-82153-1-19.
[148] M. Zhang , J. Chen , J. Liu , et al. (2022), LogST: Log Semi-supervised Anomaly Detection Based on Sentence-BERT. 2022 7th International Conference on Signal and Image Processing (ICSIP) pp 356–361, doi:10.3390/electronics12173580.
[149] X. Zhang, Y. Xu, Q. Lin, et al., logRobust 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, pp. 807–817, 2019, doi:10.1145/3338906.3338931.
[150] Z. Zhao, W. Niu, X. Zhang, et al., Trine: Syslog anomaly detection with three transformer encoders in one generative adversarial network, Applied Intelligence, 2021–2022, 2021, doi:10.1007/s10489-021-02863-9.
[151] Z. Zheng, L. Yu, W. Tang, et al., Coanalysis of RAS log and job log on Blue Gene/P, in 2011 IEEE International Parallel & Distributed Processing Symposium, pp. 840–851, 2011, doi:10.1109/IPDPS.2011.83.
[152] P. Zhou, Y. Wang, Z. Li, et al., Logsayer: Log pattern-driven cloud component anomaly diagnosis with machine learning, in 2020 IEEE/ACM 28th International Symposium on Quality-of-Service, pp. 1–10, 2020, doi:10.1109/IWQoS49365.2020.9212954.
[153] B. Zhu, J. Li, R. Gu, et al., An approach to cloud platform log anomaly detection based on natural language processing and LSTM, in 2020 3rd International Conference on Algorithms, Computing and Artificial Intelligence, pp. 1–7, 2020, doi:10.1145/3446132.3446415.
