References
- C. Wang, X. Yu, L. Xu, Z. Wang, and W. Wang, "Multimodal semantic communication accelerated bidirectional caching for 6G MEC," Future Generation Computer Systems, vol. 140, pp. 225-237, 2023. https://doi.org/10.1016/j.future.2022.10.036
- L. Li, Q. Cheng, X. Tang, T. Bai, W. Chen, Z. Ding, and Z. Han, "Resource allocation for NOMA-MEC systems in ultra-dense networks: a learning aided mean-field game approach," IEEE Transactions on Wireless Communications, vol. 20, no. 3, pp. 1487-1500, 2021. https://doi.org/10.1109/TWC.2020.3033843
- A. S. Almogren, "Intrusion detection in Edge-of-Things computing," Journal of Parallel and Distributed Computing, vol. 137, pp. 259-265, 2020. https://doi.org/10.1016/j.jpdc.2019.12.008
- X. Zhao, G. Huang, J. Jiang, L. Gao, and M. Li, "Research on lightweight anomaly detection of multimedia traffic in edge computing," Computers & Security, vol. 111, article no. 102463, 2021. https://doi.org/10.1016/j.cose.2021.102463
- M. Arjovsky, S. Chintala, and L. Bottou, "Wasserstein GAN," 2017 [Online]. Available: https://arxiv.org/abs/1701.07875.
- B. Riyaz and S. Ganapathy, "A deep learning approach for effective intrusion detection in wireless networks using CNN," Soft Computing, vol. 24, pp. 17265-17278, 2020. https://doi.org/10.1007/s00500-020-05017-0
- K. Jiang, W. Wang, A. Wang, and H. Wu, "Network intrusion detection combined hybrid sampling with deep hierarchical network," IEEE Access, vol. 8, pp. 32464-32476, 2020. https://doi.org/10.1109/ACCESS.2020.2973730
- I. Idrissi, M. Boukabous, M. Azizi, O. Moussaoui, and H. El Fadili, "Toward a deep learning-based intrusion detection system for IoT against botnet attacks," IAES International Journal of Artificial Intelligence, vol. 10, no. 1, pp. 110-120, 2021. https://doi.org/10.11591/ijai.v10.i1.pp110-120
- A. D. Smith, "Event detection in educational records: an application of big data approaches," International Journal of Business and Systems Research, vol. 15, no. 3, pp. 271-291, 2021. https://doi.org/10.1504/IJBSR.2021.114936
- A. Amouri, V. T. Alaparthy, and S. D. Morgera, "A machine learning based intrusion detection system for mobile Internet of Things," Sensors, vol. 20, no. 2, article no. 461, 2020. https://doi.org/10.3390/s20020461
- R. Kumar, P. Kumar, R. Tripathi, G. P. Gupta, S. Garg, and M. M. Hassan, "A distributed intrusion detection system to detect DDoS attacks in blockchain-enabled IoT network," Journal of Parallel and Distributed Computing, vol. 164, pp. 55-68, 2022. https://doi.org/10.1016/j.jpdc.2022.01.030
- X. Jiang, H. Zhang, J. Xu, W. Wu, and X. Xie, "Abnormal network data mining model based on deep training learning," International Journal of Internet Protocol Technology, vol. 13, no. 4, pp. 228-236, 2020. https://doi.org/10.1504/IJIPT.2020.110314
- A. Al and M. Dener, "STL-HDL: a new hybrid network intrusion detection system for imbalanced dataset on big data environment," Computers & Security, vol. 110, article no. 102435, 2021. https://doi.org/10.1016/j.cose.2021.102435
- D. Tang, J. Chen, X. Wang, S. Zhang, and Y. Yan, "A new detection method for LDoS attacks based on data mining," Future Generation Computer Systems, vol. 128, pp. 73-87, 2022. https://doi.org/10.1016/j.future.2021.09.039
- J. Fei, Q. Yao, M. Chen, X. Wang, and J. Fan, "The abnormal detection for network traffic of power IoT based on device portrait," Scientific Programming, vol. 2020, article no. 8872482, 2020. https://doi.org/10.1155/2020/8872482
- S. J. Lee, P. D. Yoo, A. T. Asyhari, Y. Jhi, L. Chermak, C. Y. Yeun, and K. Taha, "IMPACT: impersonation attack detection via edge computing using deep autoencoder and feature abstraction," IEEE Access, vol. 8, pp. 65520-65529, 2020.
- M. Eskandari, Z. H. Janjua, M. Vecchio, and F. Antonelli, "Passban IDS: an intelligent anomaly-based intrusion detection system for IoT edge devices," IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6882- 6897, 2020. https://doi.org/10.1109/JIOT.2020.2970501
- X. An, X. Zhou, X. Lu, F. Lin, and L. Yang, "Sample selected extreme learning machine based intrusion detection in fog computing and MEC," Wireless Communications and Mobile Computing, vol. 2018, article no. 7472095, 2018. https://doi.org/10.1155/2018/7472095
- L. F. Maimo, A. L. P. Gomez, F. J. G. Clemente, M. G. Perez, and G. M. Perez, "A self-adaptive deep learning-based system for anomaly detection in 5G networks," IEEE Access, vol. 6, pp. 7700-7712, 2018. https://doi.org/10.1109/ACCESS.2018.2803446
- B. Hussain, Q. Du, S. Zhang, A. Imran, and M. A. Imran, "Mobile edge computing-based data-driven deep learning framework for anomaly detection," IEEE Access, vol. 7, pp. 137656-137667, 2019. https://doi.org/10.1109/ACCESS.2019.2942485
- B. Hussain, Q. Du, A. Imran, and M. A. Imran, "Artificial intelligence-powered mobile edge computing-based anomaly detection in cellular networks," IEEE Transactions on Industrial Informatics, vol. 16, no. 8, pp. 4986-4996, 2020. https://doi.org/10.1109/TII.2019.2953201
- C. A. De Souza, C. B. Westphall, and R. B. Machado, "Two-step ensemble approach for intrusion detection and identification in IoT and fog computing environments," Computers & Electrical Engineering, vol. 98, article no. 107694, 2022. https://doi.org/10.1016/j.compeleceng.2022.107694
- H. Bangui and B. Buhnova, "Lightweight intrusion detection for edge computing networks using deep forest and bio-inspired algorithms," Computers and Electrical Engineering, vol. 100, article no. 107901, 2022. https://doi.org/10.1016/j.compeleceng.2022.107901
- W. Zhong, N. Yu, and C. Ai, "Applying big data based deep learning system to intrusion detection," Big Data Mining and Analytics, vol. 3, no. 3, pp. 181-195, 2020. https://doi.org/10.26599/BDMA.2020.9020003
- S. Garg, R. Singh, M. S. Obaidat, V. K. Bhalla, and B. Sharma, "Statistical vertical reduction-based data abridging technique for big network traffic dataset," International Journal of Communication Systems, vol. 33, no. 4, article no. e4249, 2020. https://doi.org/10.1002/dac.4249
- A. Abid and F. Jemili, "Intrusion detection based on graph oriented big data analytics," Procedia Computer Science, vol. 176, pp. 572-581, 2020. https://doi.org/10.1016/j.procs.2020.08.059
- F. Jin, M. Chen, W. Zhang, Y. Yuan, and S. Wang, "Intrusion detection on internet of vehicles via combining log-ratio oversampling, outlier detection and metric learning," Information Sciences, vol. 579, pp. 814-831, 2021. https://doi.org/10.1016/j.ins.2021.08.010
- L. Gong, X. Zhang, T. Chen, and L. Zhang, "Recognition of disease genetic information from unstructured text data based on bilstm-crf for molecular mechanisms," Security and Communication Networks, vol. 2021, article no. 6635027, 2021. https://doi.org/10.1155/2021/6635027
- M. A. Bou-Rabee, M. Y. Naz, I. E. Albalaa, and S. A. Sulaiman, "BiLSTM network-based approach for solar irradiance forecasting in continental climate zones," Energies, vol. 15, no. 6, article no. 2226, 2022. https://doi.org/10.3390/en15062226
- Y. Zhan, S. Sun, X. Li, and F. Wang, "Combined remaining life prediction of multiple bearings based on EEMD-BILSTM," Symmetry, vol. 14, no. 2, article no. 251, 2022. https://doi.org/10.3390/sym14020251
- Y. L. Miao, W. F. Cheng, Y. C. Ji, S. Zhang, and Y. L. Kong, "Aspect-based sentiment analysis in Chinese based on mobile reviews for BiLSTM-CRF," Journal of Intelligent & Fuzzy Systems, vol. 40, no. 5, pp. 8697-8707, 2021. https://doi.org/10.3233/JIFS-192078
- K. Greff, R. K. Srivastava, J. Koutnik, B. R. Steunebrink, and J. Schmidhuber, "LSTM: a search space odyssey," 2017 [Online]. Available: https://arxiv.org/abs/1503.04069.
- D. Ma, Y. Guo, and S. Ma, "Short-term subway passenger flow prediction based on GCN-BiLSTM," IOP Conference Series: Earth and Environmental Science, vol. 693, no. 1, article no. 012005, 2021. https://doi.org/10.1088/1755-1315/693/1/012005
- S. Choudhary and N. Kesswani, "Analysis of KDD-Cup'99, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT," Procedia Computer Science, vol. 167, pp. 1561-1573, 2020. https://doi.org/10.1016/j.procs.2020.03.367
- V. Kanimozhi and T. Prem Jacob, "Artificial intelligence based network intrusion detection with hyperparameter optimization tuning on the realistic cyber dataset CSE-CIC-IDS2018 using cloud computing," ICT Express, vol. 5, no. 3, pp. 211-214, 2019. https://doi.org/10.1016/j.icte.2019.03.003