Page 161 - 《软件学报》2026年第7期
P. 161

2846                                                       软件学报  2026  年第  37  卷第  7  期


                  [8]   Di Gennaro G, Buonanno A, Palmieri FAN. Considerations about learning Word2Vec. The Journal of Supercomputing, 2021, 77(1–2):
                     12320–12335. [doi: 10.1007/s11227-021-03743-2]
                  [9]   Brochier R, Guille A, Velcin J. Global vectors for node representations. In: Proc. of the 2019 World Wide Web Conf. San Francisco:
                     ACM, 2019. 2587–2593. [doi: 10.1145/3308558.3313595]
                 [10]   Joulin A, Grave E, Bojanowski P, Douze M, Jégou H, Mikolov T. FastText.zip: Compressing text classification models. arXiv:1612.
                     03651, 2016.
                 [11]   Guo HX, Yuan SH, Wu XT. LogBERT: Log anomaly detection via BERT. In: Proc. of the 2021 Int’l Joint Conf. on Neural Networks.
                     Shenzhen: IEEE, 2021. 1–8. [doi: 10.1109/IJCNN52387.2021.9534113]
                 [12]   Almodovar C, Sabrina F, Karimi S, Azad S. LogFit: Log anomaly detection using fine-tuned language models. IEEE Trans. on Network
                     and Service Management, 2024, 21(2): 1715–1723. [doi: 10.1109/TNSM.2024.3358730]
                 [13]   Liang Y, Zhang YY, Xiong H, Sahoo R. Failure prediction in IBM BlueGene/L event logs. In: Proc. of the 7th IEEE Int’l Conf. on Data
                     Mining. Omaha: IEEE, 2007. 583–588. [doi: 10.1109/ICDM.2007.46]
                 [14]   Liu ZL, Qin T, Guan XH, Jiang HZ, Wang CX. An integrated method for anomaly detection from massive system logs. IEEE Access,
                     2018, 6: 30602–30611. [doi: 10.1109/access.2018.2843336]
                 [15]   Li T, Ma JF, Pei QQ, Shen YL, Lin C, Ma SQ, Obaidat MS. AClog: Attack chain construction based on log correlation. In: Proc. of the
                     2019 IEEE Global Communications Conf. Waikoloa: IEEE, 2019. 1–6. [doi: 10.1109/GLOBECOM38437.2019.9013518]
                 [16]   Ying S, Wang BM, Wang L, Li QS, Zhao YS, Shang JG, Huang H, Cheng GL, Yang Z, Geng JY. An improved KNN-based efficient log
                     anomaly detection method with automatically labeled samples. ACM Trans. on Knowledge Discovery from Data (TKDD), 2021, 15(3):
                     34. [doi: 10.1145/3441448]
                 [17]   Landauer M, Wurzenberger M, Skopik F, Stanni G, Filzmoser P. Dynamic log file analysis: An unsupervised cluster evolution approach
                     for anomaly detection. Computers & Security, 2018, 79: 94–116. [doi: 10.1016/j.cose.2018.08.009]
                 [18]   Lin QW, Zhang HY, Lou JG, Zhang Y, Chen XW. Log clustering based problem identification for online service systems. In: Proc. of the
                     38th Int’l Conf. on Software Engineering Companion. Austin: IEEE, 2016. 102–111.
                 [19]   Lou JG, Fu Q, Yang SQ, Xu Y, Li J. Mining invariants from console logs for system problem detection. In: Proc. of the 2010 USENIX
                     Annual Technical Conf. Boston: ACM, 2010. 24.
                 [20]   Zhang B, Zhang HY, Moscato P, Zhang AZ. Anomaly detection via mining numerical workflow relations from logs. In: Proc. of the 2020
                     Int’l Symp. on Reliable Distributed Systems. Shanghai: IEEE, 2020. 195–204. [doi: 10.1109/SRDS51746.2020.00027]
                 [21]   Debnath B, Solaimani M, Gulzar MAG, Arora N, Lumezanu C, Xu JW, Zong B, Zhang H, Jiang GF, Khan L. Loglens: A real-time log
                     analysis system. In: Proc. of the 38th IEEE Int’l Conf. on Distributed Computing Systems. Vienna: IEEE, 2018. 1052–1062. [doi: 10.
                     1109/ICDCS.2018.00105]
                 [22]   Jin YJ, Qiu CL, Sun L, Peng X, Zhou JN. Anomaly detection in time series via robust PCA. In: Proc. of the 2nd IEEE Int’l Conf. on
                     Intelligent Transportation Engineering. Singapore: IEEE, 2017. 352–355. [doi: 10.1109/ICITE.2017.8056937]
                 [23]   Salman M, Husna D, Apriliani SG, Pinem JG. Anomaly based detection analysis for intrusion detection system using big data technique
                     with  learning  vector  quantization  (LVQ)  and  principal  component  analysis  (PCA).  In:  Proc.  of  the  2018  Int’l  Conf.  on  Artificial
                     Intelligence and Virtual Reality. Nagoya: ACM, 2018. 20–23. [doi: 10.1145/3293663.3293683]
                 [24]   Carter  J,  Mancoridis  S,  Galinkin  E.  Fast,  lightweight  IoT  anomaly  detection  using  feature  pruning  and  PCA.  In:  Proc.  of  the  37th
                     ACM/SIGAPP Symp. on Applied Computing. ACM, 2022, 133–138. [doi: 10.1145/3477314.3508377]
                 [25]   Jin  MX,  Lv  AR,  Zhu  YP,  Wen  ZJ,  Zhong  YB,  Zhao  ZX,  Wu  J,  Li  HJ,  He  HH,  Chen  FY.  An  anomaly  detection  algorithm  for
                     microservice architecture based on robust principal component analysis. IEEE Access, 2020, 8: 226397–226408. [doi: 10.1109/ACCESS.
                     2020.3044610]
                 [26]   Du M, Li FF, Zheng GN, Srikumar V. DeepLog: Anomaly detection and diagnosis from system logs through deep learning. In: Proc. of
                     the 2017 ACM SIGSAC Conf. on Computer and Communications Security. Dallas: ACM, 2017. 1285–1298. [doi: 10.1145/3133956.
                     3134015]
                 [27]   Du M, Chen Z, Liu C, Oak R, Song D. Lifelong anomaly detection through unlearning. In: Proc. of the 2019 ACM SIGSAC Conf. on
                     Computer and Communications Security. London: ACM, 2019. 1283–1297. [doi: 10.1145/3319535.3363226]
                 [28]   Zhou JW, Qian YJ, Zou QT, Liu P, Xiang JW. DeepSyslog: Deep anomaly detection on Syslog using sentence embedding and metadata.
                     IEEE Trans. on Information Forensics and Security, 2022, 17: 3051–3061. [doi: 10.1109/TIFS.2022.3201379]
                 [29]   Bursic S, Cuculo V, D’Amelio A. Anomaly detection from log files using unsupervised deep learning. In: Proc. of the 2020 Int’l Symp.
                     on Formal Methods. Porto: Springer, 2020. 200–207. [doi: 10.1007/978-3-030-54994-7_15]
                 [30]   Baril X, Coustié O, Mothe J, Teste O. Application performance anomaly detection with LSTM on temporal irregularities in logs. In: Proc.
   156   157   158   159   160   161   162   163   164   165   166