Abstract
Temporal Convolution Network(TCN) has recently been introduced in the cybersecurity field, where two types of TCNs that consider causal relationships are used: causal TCN and non-causal TCN. Previous researchers have utilized causal and non-causal TCNs separately. Causal TCN can predict real-time outcomes, but it ignores traffic data from the time when the detection is activated. Non-causal TCNs can forecast results more globally, but they are less real-time. Employing either causal TCN or non-causal TCN individually has its drawbacks, and overcoming these shortcomings has become an important topic.
In this research, we propose a method that combines causal and non-causal TCN in a contingent form to improve detection accuracy, maintain real-time performance, and prevent long detection time. Additionally, we use two datasets to evaluate the performance of the proposed method: NSL-KDD, a well-known dataset for evaluating network intrusion detection systems, and MQTT-IoT-2020, which simulates the MQTT protocol, a standard protocol for IoT machine-to-machine communication. The proposed method in this research increased the detection time by about 0.1ms compared to non-causal TCN when using NSL-KDD, but the accuracy improved by about 1.5%, and the recall improved by about 4%. For MQTT-IoT-2020, the accuracy improved by about 3%, and the recall improved by about 7% compared to causal TCN, but the accuracy decreased by about 1% compared to non-causal TCN. The required time was shortened by 30ms (around 30%), and the recall was improved by about 7%.
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References
Cheng, P., Xu, K., Li, S., et al.: TCAN-IDS: intrusion detection system for internet of vehicle using temporal convolutional attention network. Symmetry 14(2), 310 (2022). https://doi.org/10.3390/sym14020310
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)
Ahmad, Z., Shahid Khan, A., Wai Shiang, C., et al.: Network intrusion detection system: a systematic study of machine learning and deep learning approaches. Trans. Emerg. Telecommun. Technol. 32(1), e4150 (2021). https://doi.org/10.1002/ett.4150
Kim, A., Park, M., Lee, D.H.: AI-IDS: application of deep learning to real-time Web intrusion detection. IEEE Access 8, 70245–70261 (2020). https://doi.org/10.1109/ACCESS.2020.2986882
Gopali, S., Abri, F., Siami-Namini, S., et al.: A comparative study of detecting anomalies in time series data using LSTM and TCN models. arXiv preprint arXiv:2112.09293 (2021)
Thill, M., Konen, W., Bäck, T.: Time series encodings with temporal convolutional networks. In: Filipič, B., Minisci, E., Vasile, M. (eds.) BIOMA 2020. LNCS, vol. 12438, pp. 161–173. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63710-1_13
Derhab, A., Aldweesh, A., Emam, A.Z., et al.: Intrusion detection system for internet of things based on temporal convolution neural network and efficient feature engineering. Wirel. Commun. Mob. Comput. 2020, 1–16 (2020)
Fu, N., Kamili, N., Huang, Y., et al.: A novel deep intrusion detection model based on a convolutional neural network. Aust. J. Intell. Inf. Process. Syst. 15(2), 52–59 (2019)
Ashoor, A.S., Gore, S.: Importance of intrusion detection system (IDS). Int. J. Sci. Eng. Res. 2(1), 1–4 (2011)
Liao, H.J., Lin, C.H.R., Lin, Y.C., et al.: Intrusion detection system: a comprehensive review. J. Netw. Comput. Appl. 36(1), 16–24 (2013)
Liu, Y., Dong, H., Wang, X., et al.: Time series prediction based on temporal convolutional network. In: 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS), pp 300–305. IEEE (2019)
Xu, B., Lu, C., Guo, Y., et al.: Discriminative multi-modality speech recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14433–14442 (2020)
He, Y., Zhao, J.: Temporal convolutional networks for anomaly detection in time series. In: Journal of Physics: Conference Series, vol. 1213, no. 4, p. 042050. IOP Publishing (2019)
Ghulam Mohi-ud-Din, December 29, 2018, “NSL-KDD”, IEEE Dataport. https://doi.org/10.21227/425a-3e55
Lea, C., Vidal, R., Reiter, A., Hager, G.D.: Temporal convolutional networks: a unified approach to action segmentation. In: Hua, G., Jégou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 47–54. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49409-8_7
Raiyn, J.: A survey of cyber attack detection strategies. Int. J. Secur. Appl. 8(1), 247–256 (2014)
Kaloudi, N., Li, J.: The AI-based cyber threat landscape: a survey. ACM Comput. Surv. (CSUR) 53(1), 1–34 (2020)
Ding, Y., Zhai, Y.: Intrusion detection system for NSL-KDD dataset using convolutional neural networks. In: Proceedings of the 2018 2nd International Conference on Computer Science and Artificial Intelligence, pp. 81–85 (2018)
Belgrana, F.Z., Benamrane, N., Hamaida, M.A., et al.: Network intrusion detection system using neural network and condensed nearest neighbors with selection of NSL-KDD influencing features. In: 2020 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS), pp. 23–29. IEEE (2021)
Chaibi, N., Atmani, B., Mokaddem, M.: Deep learning approaches to intrusion detection: a new performance of ANN and RNN on NSL-KDD. In: Proceedings of the 1st International Conference on Intelligent Systems and Pattern Recognition, pp. 45–49 (2020)
Muhuri, P.S., Chatterjee, P., Yuan, X., et al.: Using a long short-term memory recurrent neural network (LSTM-RNN) to classify network attacks. Information 11(5), 243 (2020)
Tang, T.A., Mhamdi, L., McLernon, D., et al.: Deep recurrent neural network for intrusion detection in SDN-based networks. In: 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), pp. 202–206. IEEE (2018)
Xu, W., Jang-Jaccard, J., Singh, A., et al.: Improving performance of autoencoder-based network anomaly detection on NSL-KDD dataset. IEEE Access 9, 140136–140146 (2021)
Chen, Z., Yeo, C.K., Lee, B.S., et al.: Autoencoder-based network anomaly detection. In: 2018 Wireless Telecommunications Symposium (WTS), pp. 1–5. IEEE (2018)
Yin, C., Zhu, Y., Fei, J., et al.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017)
Althubiti, S.A., Jones, E.M., Roy, K.: LSTM for anomaly-based network intrusion detection. In: 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), pp. 1–3. IEEE (2018)
Selvin, S., Vinayakumar, R., Gopalakrishnan, E.A., et al.: Stock price prediction using LSTM, RNN and CNN-sliding window model. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1643–1647. IEEE (2017)
Pawar, K., Jalem, R.S., Tiwari, V.: Stock market price prediction using LSTM RNN. In: Rathore, V.S., Worring, M., Mishra, D.K., Joshi, A., Maheshwari, S. (eds.) Emerging Trends in Expert Applications and Security. AISC, vol. 841, pp. 493–503. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-2285-3_58
Hindy, H., Bayne, E., Bures, M., Atkinson, R., Tachtatzis, C., Bellekens, X.: Machine learning based IoT intrusion detection system: an MQTT case study (MQTT-IoT-IDS2020 dataset). In: Ghita, B., Shiaeles, S. (eds.) INC 2020. LNNS, vol. 180, pp. 73–84. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64758-2_6
Yao, C., Yang, Y., Yang, J., et al.: A network security situation prediction method through the use of improved TCN and BiDLSTM. Math. Probl. Eng. 2022 (2022)
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This work is partially supported by JSPS international scientific exchanges between Japan and India (Bilateral Program DTS-JSPS).
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He, P., Zhang, H., Feng, Y., Sakurai, K. (2023). A Design of Network Attack Detection Using Causal and Non-causal Temporal Convolutional Network. In: Yung, M., Chen, C., Meng, W. (eds) Science of Cyber Security . SciSec 2023. Lecture Notes in Computer Science, vol 14299. Springer, Cham. https://doi.org/10.1007/978-3-031-45933-7_30
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