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周俊伟 等: PCLog: 近端策略优化与行为克隆自适应日志异常检测 2847

of the 29th ACM Int’l Conf. on Information & Knowledge Management. ACM, 2020. 1961–1964. [doi: 10.1145/3340531.3412157]
[31] Li XY, Chen PF, Jing LX, He ZL, Yu GB. SwissLog: Robust and unified deep learning based log anomaly detection for diverse faults. In:
Proc. of the 31st IEEE Int’l Symp. on Software Reliability Engineering. Coimbra: IEEE, 2020. 92–103. [doi: 10.1109/ISSRE5003.2020.
00018]
[32] Li Y, Sun JC, Huang WG, Tian XH. Detecting anomaly in large-scale network using mobile crowdsourcing. In: Proc. of the 2019 IEEE
Conf. on Computer Communications. Paris: IEEE, 2019. 2179–2187. [doi: 10.1109/INFOCOM.2019.8737541]
[33] Le VH, Zhang HY. Log-based anomaly detection without log parsing. In: Proc. of the 36th IEEE/ACM Int’l Conf. on Automated
Software Engineering. Melbourne: IEEE, 2021. 492–504. [doi: 10.1109/ASE51524.2021.9678773]
[34] Fu YY, Liang K, Xu J. MLog: Mogrifier LSTM-based log anomaly detection approach using semantic representation. IEEE Trans. on
Services Computing, 2023, 16(5): 3537–3549. [doi: 10.1109/TSC.2023.3289488]
[35] Guo HC, Yang J, Liu JH, Bai JQ, Wang BY, Li ZJ, Zheng TQ, Zhang B, Peng JR, Tian Q. LogFormer: A pre-train and tuning pipeline
for log anomaly detection. In: Proc. of the 38th AAAI Conf. on Artificial Intelligence. Vancouver: AAAI, 2024. 135–143. [doi: 10.1609/
aaai.v38i1.27764]
[36] Yan LJ, Luo C, Shao R. Discrete log anomaly detection: A novel time-aware graph-based link prediction approach. Information Sciences,
2023, 647: 119576. [doi: 10.1016/j.ins.2023.119576]
[37] He ZY, Tang YN, Zhao KQ, Liu JM, Chen W. Graph-based log anomaly detection via adversarial training. In: Proc. of the 9th Int’l
Symp. on Dependable Software Engineering: Theories, Tools, and Applications. Nanjing: Springer, 2023. 55–71. [doi: 10.1007/978-981-
99-8664-4_4]
[38] Li Z, Shi JY, van Leeuwen M. Graph neural networks based log anomaly detection and explanation. In: Proc. of the 46th IEEE/ACM Int’l
Conf. on Software Engineering: Companion Proc. Lisbon: ACM, 2024. 306–307. [doi: 10.1145/3639478.3643084]
[39] Chu GJ, Wang JY, Qi Q, Sun HF, Zhuang ZR, He B, Jing YH, Zhang L, Liao JX. Anomaly detection on interleaved log data with
semantic association mining on log-entity graph. IEEE Trans. on Software Engineering, 2025, 51(2): 581–594. [doi: 10.1109/TSE.2025.
3527856]
[40] Xie YX, Yang K. Log anomaly detection by adversarial autoencoders with graph feature fusion. IEEE Trans. on Reliability, 2024, 73(1):
637–649. [doi: 10.1109/TR.2023.3305376]
[41] Xia B, Yin JJ, Xu J, Li Y. LogGAN: A sequence-based generative adversarial network for anomaly detection based on system logs. In:
Proc. of the 2nd Int’l Conf. on Science of Cyber Security. Nanjing: Springer, 2019. 61–76. [doi: 10.1007/978-3-030-34637-9_5]
[42] Tao SM, Liu YL, Meng WB, Ren ZM, Yang H, Chen X, Zhang L, Xie YM, Su C, Oiao X, Tian WN, Zhu YC, Han T, Qin Y, Li Y.
Biglog: Unsupervised large-scale pre-training for a unified log representation. In: Proc. of the 31st IEEE/ACM Int’l Symp. on Quality of
Service. Orlando: IEEE, 2023. 1–11. [doi: 10.1109/IWQoS57198.2023.10188759]
[43] Xu JJL, Yang RC, Huo YT, Zhang CY, He PJ. DivLog: Log parsing with prompt enhanced in-context learning. In: Proc. of the 46th
IEEE/ACM Int’l Conf. on Software Engineering. Lisbon: ACM, 2024. 199. [doi: 10.1145/3597503.3639155]
[44] Liu YL, Tao SM, Meng WB, Wang JY, Ma WB, Chen YH, Zhao YQ, Yang H, Jiang YF. Interpretable online log analysis using large
language models with prompt strategies. In: Proc. of the 32nd IEEE/ACM Int’l Conf. on Program Comprehension. Lisbon: ACM, 2024.
35–46. [doi: 10.1145/3643916.3644408]
[45] Ma LP, Yang WD, Xu B, Jiang SH, Fei B, Liang JQ, Zhou MJ, Xiao YH. KnowLog: Knowledge enhanced pre-trained language model
for log understanding. In: Proc. of the 46th IEEE/ACM Int’l Conf. on Software Engineering. Lisbon: ACM, 2024. 32. [doi: 10.1145/
3597503.3623304]
[46] Wang XH, Song JX, Zhang X, Tang JS, Gao WH, Lin QW. LogOnline: A semi-supervised log-based anomaly detector aided with online
learning mechanism. In: Proc. of the 38th IEEE/ACM Int’l Conf. on Automated Software Engineering (ASE). Luxembourg: IEEE, 2023.
141–152. [doi: 10.1109/ASE56229.2023.00043]
[47] Han SB, Wu QH, Zhang H, Qin B, Hu JK, Shi XG, Liu LF, Yin X. Log-based anomaly detection with robust feature extraction and
online learning. IEEE Trans. on Information Forensics and Security, 2021, 16: 2300–2311. [doi: 10.1109/TIFS.2021.3053371]
[48] Yuan YL, Adhatarao SS, Lin MK, Yuan YC, Liu ZL, Fu XM. ADA: Adaptive deep log anomaly detector. In: Proc. of the 2020 IEEE
Conf. on Computer Communications. Toronto: IEEE, 2020. 2449–2458. [doi: 10.1109/INFOCOM41043.2020.9155487]
[49] Duan XY, Ying S, Yuan WL, Cheng HL, Yin X. QLLog: A log anomaly detection method based on Q-learning algorithm. Information
Processing & Management, 2021, 58(3): 102540. [doi: 10.1016/j.ipm.2021.102540]
[50] Zhou JW, Gao YY, Zhu Y, Yu XT, Yang YC, Tan C, Xiang JW. DRLLog: Deep reinforcement learning for online log anomaly
detection. IEEE Trans. on Network and Service Management, 2025, 22(3): 2382–2395. [doi: 10.1109/tnsm.2025.3542595]
[51] Liu Quan, Yan Jie, Wu Lan. Offline reinforcement learning method with diffusion model and expectation maximization. Ruan Jian Xue