KBJNet: Kinematic Bi-Joint Temporal Convolutional Network Attention for Anomaly Detection in Multivariate Time Series Data
References
- Ahmed, 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 - Amer, 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. 1–12.
- Bandaragoda, 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
- Boniol, P, Palpanas, T, Meftah, M and Remy, E. 2020. GraphAn. Proceedings of the VLDB Endowment, 13(12): 2941–2944. DOI: 10.14778/3415478.3415514
- Breunig, 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
- Budiarto, 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
- Bulusu, S, Kailkhura, B, Li, B, Varshney, PK and Song, D. 2020. Anomalous example detection in deep learning: A survey. IEEE Access, 8: 132330–132347. DOI: 10.1109/ACCESS.2020.3010274
- Chatterjee, 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
- Chen, 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 - Chen, 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): 9179–9189. DOI: 10.1109/JIOT.2021.3100509
- Chowdhury, 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): 1587–1606. DOI: 10.1007/s11069-017-2835-1
- Darban, ZZ, Webb, GI, Pan, S, Aggarwal, CC and Salehi, M. 2022. Deep learning for time series anomaly detection: A survey. arXiv:2211.05244.
- Dau, 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): 1293–1305. DOI: 10.1109/JAS.2019.1911747
- Deng, A and Hooi, B. 2021. Graph neural networkbased anomaly detection in multivariate time series. Proceedings of the AAAI Conference on Artificial Intelligence, 35(5): 4027–4035. DOI: 10.1609/aaai.v35i5.16523
- Dhiman, 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): 3462–3469. DOI: 10.1109/TEC.2021.3075897
- Ergen, T and Kozat, SS. 2020. Unsupervised anomaly detection with LSTM neural networks. IEEE Transactions on Neural Networks and Learning Systems, 31(8): 3127–3141. DOI: 10.1109/TNNLS.2019.2935975
- Finn, 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. 1126–1135.
- Goldstein, M and Dengel, A. 2012. Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm, KI-2012: poster and demo track, 1: 59–63.
- Hu, 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): 218–233. DOI: 10.1109/TKDE.2018.2882404
- Huang, 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): 2064–2076. DOI: 10.1109/TNSM.2020.3034647
- Hundman, 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. 387–395. DOI: 10.1145/3219819.3219845
- Jin, 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. 352–355. DOI: 10.1109/ICITE.2017.8056937
- Kingsbury, K and Alvaro, P. 2020. Elle. Proceedings of the VLDB Endowment, 14(3): 268–280. DOI: 10.14778/3430915.3430918
- Kiss, 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
- Li, 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. 703–716. DOI: 10.1007/978-3-030-30490-4_56 - Li, 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
- Luo, 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
- Malhotra, 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 . - Mathur, 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 - Moody, G and Mark, R. 2001. The impact of the MIT-BIH arrhythmia database. IEEE Engineering in Medicine and Biology Magazine, 20(3): 45–50. DOI: 10.1109/51.932724
- Pan, X, Tan, J, Kavulya, S, Gandhi, R and Narasimhan, P. 2010. Ganesha, ACM SIGMETRICS Performance Evaluation Review, 37(3): 8–13. DOI: 10.1145/1710115.1710118
- Park, 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): 1544–1551. DOI: 10.1109/LRA.2018.2801475
- Patcha, A and Park, J-M. 2007. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12): 3448–3470. DOI: 10.1016/j.comnet.2007.02.001
- Provotar, 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
- Qu, 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
- Ramaswamy, S, Rastogi, R and Shim, K. 2000. Efficient algorithms for mining outliers from large data sets. ACM SIGMOD Record, 29(2): 427–438. DOI: 10.1145/335191.335437
- Salem, 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): 20–45. DOI: 10.4018/ijehmc.2014010102
- Sarker, 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
- Schölkopf, B, Platt, JC, Shawe-Taylor, J, Smola, AJ and Williamson, RC. 2001. Estimating the support of a highdimensional distribution. Neural Computation, 13(7): 1443–1471. DOI: 10.1162/089976601750264965
- Shang, 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
- Shih, S-Y, Sun, F-K and yi Lee, H. 2019. Temporal pattern attention for multivariate time series forecasting. Machine Learning, 108(8–9): 1421–1441. DOI: 10.1007/s10994-019-05815-0
- Shyu, 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. - Siffer, 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
- Su, 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
- Tax, DM and Duin, RP. 2004. Support vector data description. Machine Learning, 54: 45–66. DOI: 10.1023/B:MACH.0000008084.60811.49
- Thill, 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
- Thudumu, 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
- Tran, L, Mun, MY and Shahabi, C. 2020. Real-time distance-based outlier detection in data streams. Proceedings of the VLDB Endowment, 14(2): 141–153. DOI: 10.14778/3425879.3425885
- Tuli, S, Casale, G and Jennings, NR. 2022. TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data. Proceedings of VLDB, 15(6): 1201–1214. DOI: 10.14778/3514061.3514067
- Veličković, 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 - Wang, 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): 7183–7193. DOI: 10.1016/j.aej.2021.12.061
- Wang, 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): 1411–1421. DOI: 10.1109/TITS.2020.2970295
- Xu, 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 - Yaacob, 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
- Yin, 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: 56145–56157. DOI: 10.1109/ACCESS.2022.3175516
- Yu, 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
- Zang, 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. 1059–1063. DOI: 10.1109/CCDC.2018.8407286
- Zhang, 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): 1409–1416. DOI: 10.1609/aaai.v33i01.33011409
- Zhang, 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. 1–1. DOI: 10.1109/TKDE.2021.3102110
- Zhao, 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
- Zong, 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.
DOI: https://doi.org/10.5334/dsj-2024-010 | Journal eISSN: 1683-1470
Language: English
Page range: 10 - 10
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.