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KBJNet: Kinematic Bi-Joint Temporal Convolutional Network Attention for Anomaly Detection in Multivariate Time Series Data Cover

KBJNet: Kinematic Bi-Joint Temporal Convolutional Network Attention for Anomaly Detection in Multivariate Time Series Data

Open Access
|Mar 2024

References

  1. 1Ahmed, CM, Palleti, VR and Mathur, AP. 2017. WADI. In: Proceedings of the 3rd International Workshop on Cyber-Physical Systems for Smart Water Networks, ACM. DOI: 10.1145/3055366.3055375
  2. 2Amer, M and Goldstein, M. 2012. Nearest-neighbor and clustering based anomaly detection algorithms for rapidminer. In: Proceedings of the 3rd RapidMiner Community Meeting and Conference (RCOMM 2012), pp. 112.
  3. 3Bandaragoda, TR, Ting, KM, Albrecht, D, Liu, FT and Wells, JR. 2014. Efficient anomaly detection by isolation using nearest neighbour ensemble. In: 2014 IEEE International Conference on Data Mining Workshop, IEEE. DOI: 10.1109/ICDMW.2014.70
  4. 4Boniol, P, Palpanas, T, Meftah, M and Remy, E. 2020. GraphAn. Proceedings of the VLDB Endowment, 13(12): 29412944. DOI: 10.14778/3415478.3415514
  5. 5Breunig, MM, Kriegel, H-P, Ng, RT and Sander, J. 2000. LOF. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, ACM. DOI: 10.1145/342009.335388
  6. 6Budiarto, EH, Permanasari, AE and Fauziati, S. 2019. Unsupervised anomaly detection using k-means, local outlier factor and one class SVM. In: 2019 5th International Conference on Science and Technology (ICST), IEEE. DOI: 10.1109/ICST47872.2019.9166366
  7. 7Bulusu, S, Kailkhura, B, Li, B, Varshney, PK and Song, D. 2020. Anomalous example detection in deep learning: A survey. IEEE Access, 8: 132330132347. DOI: 10.1109/ACCESS.2020.3010274
  8. 8Chatterjee, A and Ahmed, BS. 2022. IoT anomaly detection methods and applications: A survey. Internet of Things, 19: 100568. DOI: 10.1016/j.iot.2022.100568
  9. 9Chen, J, Mao, Q and Liu, D. 2020. Dual-path transformer network: Direct context-aware modeling for end-to-end monaural speech separation. In: Interspeech 2020, ISCA. DOI: 10.21437/Interspeech.2020-2205
  10. 10Chen, Z, Chen, D, Zhang, X, Yuan, Z and Cheng, X. 2022. Learning graph structures with transformer for multivariate time-series anomaly detection in IoT. IEEE Internet of Things Journal, 9(12): 91799189. DOI: 10.1109/JIOT.2021.3100509
  11. 11Chowdhury, S, Deb, A, Nurujjaman, M and Barman, C. 2017. Identification of pre-seismic anomalies of soil radon-222 signal using Hilbert–Huang transform. Natural Hazards, 87(3): 15871606. DOI: 10.1007/s11069-017-2835-1
  12. 12Darban, ZZ, Webb, GI, Pan, S, Aggarwal, CC and Salehi, M. 2022. Deep learning for time series anomaly detection: A survey. arXiv:2211.05244.
  13. 13Dau, HA, Bagnall, A, Kamgar, K, Yeh, C-CM, Zhu, Y, Gharghabi, S, Ratanamahatana, CA and Keogh, E. 2019. The UCR time series archive. IEEE/CAA Journal of Automatica Sinica, 6(6): 12931305. DOI: 10.1109/JAS.2019.1911747
  14. 14Deng, A and Hooi, B. 2021. Graph neural networkbased anomaly detection in multivariate time series. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5): 40274035. DOI: 10.1609/aaai.v35i5.16523
  15. 15Dhiman, H, Deb, D, Muyeen, SM and Kamwa, I. 2021. Wind turbine gearbox anomaly detection based on adaptive threshold and twin support vector machines. IEEE Transactions on Energy Conversion, 36(4): 34623469. DOI: 10.1109/TEC.2021.3075897
  16. 16Ergen, T and Kozat, SS. 2020. Unsupervised anomaly detection with LSTM neural networks. IEEE Transactions on Neural Networks and Learning Systems, 31(8): 31273141. DOI: 10.1109/TNNLS.2019.2935975
  17. 17Finn, C, Abbeel, P and Levine, S. 2017. Model-agnostic meta-learning for fast adaptation of deep networks, In: International conference on machine learning, PMLR, pp. 11261135.
  18. 18Goldstein, M and Dengel, A. 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm, KI-2012: poster and demo track, 1: 5963.
  19. 19Hu, W, Gao, J, Li, B, Wu, O, Du, J and Maybank, S. 2020. Anomaly detection using local kernel density estimation and context-based regression. IEEE Transactions on Knowledge and Data Engineering, 32(2): 218233. DOI: 10.1109/TKDE.2018.2882404
  20. 20Huang, S, Liu, Y, Fung, C, He, R, Zhao, Y, Yang, H and Luan, Z. 2020. HitAnomaly: Hierarchical transformers for anomaly detection in system log. IEEE Transactions on Network and Service Management, 17(4): 20642076. DOI: 10.1109/TNSM.2020.3034647
  21. 21Hundman, K, Constantinou, V, Laporte, C, Colwell, I and Soderstrom, T. 2018. Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 387395. DOI: 10.1145/3219819.3219845
  22. 22Jin, Y, Qiu, C, Sun, L, Peng, X and Zhou, J. 2017. Anomaly detection in time series via robust PCA. In: 2017 2 nd IEEE International Conference on Intelligent Transportation Engineering (ICITE), IEEE, pp. 352355. DOI: 10.1109/ICITE.2017.8056937
  23. 23Kingsbury, K and Alvaro, P. 2020. Elle. Proceedings of the VLDB Endowment, 14(3): 268280. DOI: 10.14778/3430915.3430918
  24. 24Kiss, I, Genge, B, Haller, P and Sebestyen, G. 2014. Data clustering-based anomaly detection in industrial control systems. In: 2014 IEEE 10th International Conference on Intelligent Computer Communication and Processing (ICCP), IEEE. DOI: 10.1109/ICCP.2014.6937009
  25. 25Li, D, Chen, D, Jin, B, Shi, L, Goh, J and Ng, S-K. 2019. Mad-GAN: Multivariate anomaly detection for time series data with generative adversarial networks. In: Artificial Neural Networks and Machine Learning–ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV, Springer, pp. 703716. DOI: 10.1007/978-3-030-30490-4_56
  26. 26Li, Z, Zhao, Y, Botta, N, Ionescu, C and Hu, X. 2020. COPOD: Copula-based outlier detection. In: 2020 IEEE International Conference on Data Mining (ICDM), IEEE. DOI: 10.1109/ICDM50108.2020.00135
  27. 27Luo, Y, Chen, Z and Yoshioka, T. 2020. Dual-path RNN: Efficient long sequence modeling for time-domain single-channel speech separation. In: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE. DOI: 10.1109/ICASSP40776.2020.9054266
  28. 28Malhotra, P, Ramakrishnan, A, Anand, G, Vig, L, Agarwal, P and Shroff, G. 2016. Lstm-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148.
  29. 29Mathur, AP and Tippenhauer, NO. 2016. SWaT: A water treatment testbed for research and training on ICS security. In: 2016 International Workshop on Cyber-physical Systems for Smart Water Networks (CySWater), IEEE. DOI: 10.1109/CySWater.2016.7469060
  30. 30Moody, G and Mark, R. 2001. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3): 4550. DOI: 10.1109/51.932724
  31. 31Pan, X, Tan, J, Kavulya, S, Gandhi, R and Narasimhan, P. 2010. Ganesha, ACM SIGMETRICS Performance Evaluation Review, 37(3): 813. DOI: 10.1145/1710115.1710118
  32. 32Park, D, Hoshi, Y and Kemp, CC. 2018. A multimodal anomaly detector for robot-assisted feeding using an LSTM-based variational autoencoder. IEEE Robotics and Automation Letters, 3(3): 15441551. DOI: 10.1109/LRA.2018.2801475
  33. 33Patcha, A and Park, J-M. 2007. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12): 34483470. DOI: 10.1016/j.comnet.2007.02.001
  34. 34Provotar, OI, Linder, YM and Veres, MM. 2019. Unsupervised anomaly detection in time series using LSTM-based autoencoders. In: 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT), IEEE. DOI: 10.1109/ATIT49449.2019.9030505
  35. 35Qu, Z, Su, L, Wang, X, Zheng, S, Song, X and Song, X. 2018. A unsupervised learning method of anomaly detection using GRU. In: 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), IEEE. DOI: 10.1109/BigComp.2018.00126
  36. 36Ramaswamy, S, Rastogi, R and Shim, K. 2000. Efficient algorithms for mining outliers from large data sets. ACM SIGMOD Record, 29(2): 427438. DOI: 10.1145/335191.335437
  37. 37Salem, O, Guerassimov, A, Mehaoua, A, Marcus, A and Furht, B. 2014. Anomaly detection in medical wireless sensor networks using SVM and linear regression models. International Journal of E-Health and Medical Communications, 5(1): 2045. DOI: 10.4018/ijehmc.2014010102
  38. 38Sarker, IH. 2021. Data science and analytics: An overview from data-driven smart computing, decision-making and applications perspective. SN Computer Science, 2(5). DOI: 10.1007/s42979-021-00765-8
  39. 39Schölkopf, B, Platt, JC, Shawe-Taylor, J, Smola, AJ and Williamson, RC. 2001. Estimating the support of a highdimensional distribution. Neural Computation, 13(7): 14431471. DOI: 10.1162/089976601750264965
  40. 40Shang, W, Cui, J, Song, C, Zhao, J and Zeng, P. 2018. Research on industrial control anomaly detection based on FCM and SVM. In: 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), IEEE. DOI: 10.1109/TrustCom/BigDataSE.2018.00042
  41. 41Shih, S-Y, Sun, F-K and yi Lee, H. 2019. Temporal pattern attention for multivariate time series forecasting. Machine Learning, 108(8–9): 14211441. DOI: 10.1007/s10994-019-05815-0
  42. 42Shyu, M-L, Chen, S-C, Sarinnapakorn, K and Chang, L. 2003. A novel anomaly detection scheme based on principal component classifier. Technical Report, Miami Univ Coral Gables Fl Dept of Electrical and Computer Engineering.
  43. 43Siffer, A, Fouque, P-A, Termier, A and Largouet, C. 2017. Anomaly detection in streams with extreme value theory. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM. DOI: 10.1145/3097983.3098144
  44. 44Su, Y, Zhao, Y, Niu, C, Liu, R, Sun, W and Pei, D. 2019. Robust anomaly detection for multivariate time series through stochastic recurrent neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ACM. DOI: 10.1145/3292500.3330672
  45. 45Tax, DM and Duin, RP. 2004. Support vector data description. Machine Learning, 54: 4566. DOI: 10.1023/B:MACH.0000008084.60811.49
  46. 46Thill, M, Konen, W, Wang, H and Bäck, T. 2021. Temporal convolutional autoencoder for unsupervised anomaly detection in time series. Applied Soft Computing, 112: 107751. DOI: 10.1016/j.asoc.2021.107751
  47. 47Thudumu, S, Branch, P, Jin, J and Singh, JJ. 2020. A comprehensive survey of anomaly detection techniques for high dimensional big data. Journal of Big Data, 7(1). DOI: 10.1186/s40537-020-00320-x
  48. 48Tran, L, Mun, MY and Shahabi, C. 2020. Real-time distance-based outlier detection in data streams. Proceedings of the VLDB Endowment, 14(2): 141153. DOI: 10.14778/3425879.3425885
  49. 49Tuli, S, Casale, G and Jennings, NR. 2022. TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data. Proceedings of VLDB, 15(6): 12011214. DOI: 10.14778/3514061.3514067
  50. 50Veličković, P, Cucurull, G, Casanova, A, Romero, A, Liò, P and Bengio, Y. 2018. Graph Attention Networks. International Conference on Learning Representations. Available at: https://openreview.net/forum?id=rJXMpikCZ
  51. 51Wang, B, Hua, Q, Zhang, H, Tan, X, Nan, Y, Chen, R and Shu, X. 2022. Research on anomaly detection and real-time reliability evaluation with the log of cloud platform. Alexandria Engineering Journal, 61(9): 71837193. DOI: 10.1016/j.aej.2021.12.061
  52. 52Wang, Y, Masoud, N and Khojandi, A. 2021. Real-time sensor anomaly detection and recovery in connected automated vehicle sensors. IEEE Transactions on Intelligent Transportation Systems, 22(3): 14111421. DOI: 10.1109/TITS.2020.2970295
  53. 53Xu, J, Wu, H, Wang, J and Long, M. 2022. Anomaly transformer: Time series anomaly detection with association discrepancy. In: International Conference on Learning Representations. Available at: https://openreview.net/forum?id=LzQQ89U1qm
  54. 54Yaacob, AH, Tan, IK, Chien, SF and Tan, HK. 2010. ARIMA based network anomaly detection. In: 2010 Second International Conference on Communication Software and Networks, IEEE. DOI: 10.1109/ICCSN.2010.55
  55. 55Yin, K, Yang, Y, Yao, C and Yang, J. 2022. Long-term prediction of network security situation through the use of the transformer-based model. IEEE Access, 10: 5614556157. DOI: 10.1109/ACCESS.2022.3175516
  56. 56Yu, L, Lu, Q and Xue, Y. 2023. DTAAD: Dual TCN-attention networks for anomaly detection in multivariate time series data. arXiv:2302.10753. DOI: 10.2139/ssrn.4410420
  57. 57Zang, D, Liu, J and Wang, H. 2018. Markov chain-based feature extraction for anomaly detection in time series and its industrial application. In: 2018 Chinese Control And Decision Conference (CCDC), IEEE, pp. 10591063. DOI: 10.1109/CCDC.2018.8407286
  58. 58Zhang, C, Song, D, Chen, Y, Feng, X, Lumezanu, C, Cheng, W, Ni, J, Zong, B, Chen, H and Chawla, NV. 2019. A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. Proceedings of the AAAI Conference on Artificial Intelligence, 33(1): 14091416. DOI: 10.1609/aaai.v33i01.33011409
  59. 59Zhang, Y, Chen, Y, Wang, J and Pan, Z. 2021. Unsupervised deep anomaly detection for multi-sensor timeseries signals, IEEE Transactions on Knowledge and Data Engineering, pp. 11. DOI: 10.1109/TKDE.2021.3102110
  60. 60Zhao, H, Wang, Y, Duan, J, Huang, C, Cao, D, Tong, Y, Xu, B, Bai, J, Tong, J and Zhang, Q. 2020. Multivariate time-series anomaly detection via graph attention network. In: 2020 IEEE International Conference on Data Mining (ICDM), IEEE. DOI: 10.1109/ICDM50108.2020.00093
  61. 61Zong, B, Song, Q, Min, MR, Cheng, W, Lumezanu, C, Cho, D and Chen, H. 2018. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: International Conference on Learning Representations.
Language: English
Submitted on: Jun 29, 2023
Accepted on: Oct 6, 2023
Published on: Mar 4, 2024
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Muhammad Abdan Mulia, Muhammad Bintang Bahy, Muhammad Zain Fawwaz Nuruddin Siswantoro, Nur Rahmat Dwi Riyanto, Nella Rosa Sudianjaya, Ary Mazharuddin Shiddiqi, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.