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Articles

TCN enhanced novel malicious traffic detection for IoT devices

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Pages 1322-1341 | Received 22 Dec 2021, Accepted 12 Apr 2022, Published online: 11 May 2022

References

  • Boukhtouta, A., Mokhov, S. A., Lakhdari, N. E., Debbabi, M., & Paquet, J. (2016). Network malware classification comparison using DPI and flow packet headers. Journal of Computer Virology and Hacking Techniques, 12(2), 69–100. https://doi.org/10.1007/s11416-015-0247-x
  • Cai, S., Han, D., Yin, X., Li, D., & Chang, C. C. (2022). A hybrid parallel deep learning model for efficient intrusion detection based on metric learning. Connection Science, 34(1), 1–27. https://doi.org/10.1080/09540091.2021.2024509
  • Chang, F., Ge, L., Li, S., Wu, K., & Wang, Y. (2021). Self-adaptive spatial-temporal network based on heterogeneous data for air quality prediction. Connection Science, 33(3), 427–446. https://doi.org/10.1080/09540091.2020.1841095
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
  • Fawcett, T. (2004). ROC graphs: Notes and practical considerations for researchers. Machine Learning, 31(1), 1–38.
  • Fielding, R., Gettys, J., Mogul, J., Frystyk, H., Masinter, L., Leach, P., & Berners-Lee, T. (2006). Hypertext transfer protocol–HTTP/1.1, 1999. RFC2616.
  • Gajewski, M., Batalla, J. M., Levi, A., Togay, C., Mavromoustakis, C. X., & Mastorakis, G. (2019). Two-tier anomaly detection based on traffic profiling of the home automation system. Computer Networks, 158(9), 46–60. https://doi.org/10.1016/j.comnet.2019.04.013
  • Gajewski, M., Mongay Batalla, J., Mastorakis, G., & Mavromoustakis, C. X. (2020). Anomaly traffic detection and correlation in smart home automation IoT systems. Transactions on Emerging Telecommunications Technologies, 2020 Jul 31, e4053. https://doi.org/10.1002/ett.4053
  • Garcia, L. M. (2008). Programming with libpcap-sniffing the network from our own application. Hakin9-Computer Security Magazine, 2, 2008.
  • Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics (pp. 249–256). JMLR Workshop and Conference Proceedings
  • Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN model-based approach in classification. In OTM confederated international conferences” on the move to meaningful internet systems” (pp. 986–996). Springer Berlin Heidelberg
  • Ibitoye, O., Abou-Khamis, R., Matrawy, A., & Shafiq, M. O. (2019). The threat of adversarial attacks on machine learning in network security–a survey. arXiv preprint arXiv:1911.02621.
  • IEEE (n.d.). Ieee 802.3x-1997. Retrieved from https://standards.ieee.org/standard/802_3x-1997.html.
  • Jz, A., Yu, L. A., Xfb, C., Xy, D., Gang, X. D., & Rui, Z. E. (n.d.). Model of the intrusion detection system based on the integration of spatial-temporal features – sciencedirect. Computers & Security, 89. https://doi.org/10.1016/j.cose.2019.101681
  • Kim, J., Kim, J., Kim, H., Shim, M., & Choi, E. (2020). CNN-based network intrusion detection against denial-of-service attacks. Electronics, 9(6), 916. https://doi.org/10.3390/electronics9060916
  • Lin, P., Ye, K., & Xu, C. Z. (2019). Dynamic network anomaly detection system by using deep learning techniques. In International conference on cloud computing (pp. 161–176). Springer, Cham
  • Lipton, Z. C., Berkowitz, J., & Elkan, C. (2015). A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019.
  • Liu, X., Yu, Q., Zhou, X., & Zhou, Q. (2018). Owleye: An advanced detection system of web attacks based on hmm. In 2018 ieee 16th intl conf on dependable, autonomic and secure computing, 16th intl conf on pervasive intelligence and computing, 4th intl conf on big data intelligence and computing and cyber science and technology congress (dasc/picom/datacom/cyberscitech) (pp. 200–207). IEEE
  • Liu, X., Zhang, W., Zhou, X., & Zhou, Q. (2021). MECGuard: GRU enhanced attack detection in mobile edge computing environment. Computer Communications, 172(1), 1–9. https://doi.org/10.1016/j.comcom.2021.02.022
  • Liu, Z., Japkowicz, N., Wang, R., & Tang, D. (2019). Adaptive learning on mobile network traffic data. Connection Science, 31(2), 185–214. https://doi.org/10.1080/09540091.2018.1512557
  • Madhukar, A., & Williamson, C. (2006). A longitudinal study of P2P traffic classification. In 14th ieee international symposium on modeling, analysis, and simulation (pp. 179–188). IEEE
  • Malialis, K., Devlin, S., & Kudenko, D. (2015). Distributed reinforcement learning for adaptive and robust network intrusion response. Connection Science, 27(3), 234–252. https://doi.org/10.1080/09540091.2015.1031082.
  • McCanne, S. (2011). libpcap: An architecture and optimization methodology for packet capture. Sharkfest.
  • Mondal, P. K., Sanchez, L. P. A., Benedetto, E., Shen, Y., & Guo, M. (2021). A dynamic network traffic classifier using supervised ML for a Docker-based SDN network. Connection Science, 33(3), 693–718. https://doi.org/10.1080/09540091.2020.1870437
  • Nguyen, T. T., & Armitage, G. (2008). A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys & Tutorials, 10(4), 56–76. https://doi.org/10.1109/SURV.2008.080406
  • Ning, Z., Shi, W., Xiao, L., Liang, W., & Weng, T. H. (2021). A novel approach for anti-pollution attacks in network coding. Connection Science, 33(3), 447–462. https://doi.org/10.1080/09540091.2020.1841109.
  • O'Gorman, B., Wueest, C., O'Brien, D., Cleary, G., Lau, H., Power, J. P., Corpin, M., Cox, O., Wood, P., & Wallace, S. (2019). Internet security threat report. A Report Published by SYMANTEC, 24, 32.
  • Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O. , Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., & Vanderplas, J. (2011). Scikit-learn: machine learning in python. The Journal of Machine Learning Research, 12, 2825–2830. https://doi.org/10.48550/arXiv.1201.0490
  • Porter, T. (2005). The perils of deep packet inspection. Security Focus, 6.
  • Postel, J. (1981a). Rfc0793: Transmission control protocol. RFC Editor.
  • Postel, J., et al. (1981b). Internet protocol. RFC 791.
  • Retal, S., & Idrissi, A. (2020). A fuzzy controller for an adaptive VNFs placement in 5G network architecture. International Journal of Computational Science and Engineering, 21(2), 304–314. https://doi.org/10.1504/IJCSE.2020.105743
  • Risso, F., & Degioanni, L. (2001). An architecture for high performance network analysis. In Proceedings. Sixth ieee symposium on computers and communications (pp. 686–693). IEEE
  • Rocha, E., Salvador, P., & Nogueira, A. (2011). Detection of illicit network activities based on multivariate gaussian fitting of multi-scale traffic characteristics. In 2011 ieee international conference on communications (ICC) (pp. 1–6). IEEE
  • Santos, G. L., Gomes, D., Kelner, J., Sadok, D., Silva, F. A., Endo, P. T., & Lynn, T. (2020). The internet of things for healthcare: Optimising e-health system availability in the fog and cloud. International Journal of Computational Science and Engineering, 21(4), 615–628. https://doi.org/10.1504/IJCSE.2020.106873
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61(3), 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
  • Skaruz, J., & Seredynski, F. (2007). Recurrent neural networks towards detection of SQL attacks. In Parallel and distributed processing symposium, 2007. IPDPS 2007. Ieee international. IEEE
  • Telikani, A., Gandomi, A. H., Choo, K. K.R., & Shen, J. (2021). A cost-sensitive deep learning based approach for network traffic classification. IEEE Transactions on Network and Service Management, 19(1), 1–1. https://doi.org/10.1109/TNSM.2021.3112283
  • Unb (2021). Cse-cic-ids 2018. Retrieved from http://www.unb.ca/cic/datasets/ids-2018.html.
  • Wei, W., Sheng, Y., Wang, J., Zeng, X., & Ming, Z. (2018). HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection. IEEE Access, 6(99), 1792–1806. https://doi.org/10.1109/ACCESS.2017.2780250
  • Xu, C., Chen, S., Su, J., Yiu, S. M., & Hui, L. C. (2016). A survey on regular expression matching for deep packet inspection: Applications, algorithms, and hardware platforms. IEEE Communications Surveys & Tutorials, 18(4), 2991–3029. https://doi.org/10.1109/COMST.2016.2566669
  • Zhang, Z. K., Cho, M. C. Y., Wang, C. W., Hsu, C. W., Chen, C. K., & Shieh, S. (2014). IoT Security: Ongoing Challenges and Research Opportunities. In 2014 ieee 7th international conference on service-oriented computing and applications (pp. 230–234).
  • Zhao, F., Zhang, H., Peng, J., Zhuang, X., & Na, S. G. (2020). A semi-self-taught network intrusion detection system. Neural Computing and Applications, 32(23), 17169–17179. https://doi.org/10.1007/s00521-020-04914-7