Volume 5 , Issue 1 , PP: 36-43, 2022 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud A. Zaher 1 * , Nabil M. Eldakhly 2
Doi: https://doi.org/10.54216/IJWAC.050103
Today's societies couldn't function without elaborate networks of communication. Many problems remain unresolved, but novel approaches to these problems are constantly being offered. Many of the problems plaguing existing works, such as high characteristic design cost, challenging feature selection, poor real-time performance, etc., stem from their focus on a wide range of characteristics. Worse still, the difficulty in training models due to data imbalance results in a poor detection rate for aberrant samples. To achieve a more effective and robust model, we present a multi-level feature fusion (MFFusion) model that utilizes a combination of data temporal, byte, and statistical characteristics to extract relevant information from different angles. Too far, MFFusion has outperformed the state-of-the-art on several real-world network datasets in terms of prediction performance and false alarm rate. We also use MFFusion for anomaly detection in an IoT network, using the most recent IoT malicious traffic information. The experimental results demonstrate the adaptability of MFFusion and its suitability for identifying network anomalies in an IoT context with system performance.
Network Communication , IoT , Multi-Level Fusion , Deep Learning , Machine Learning
[1] E. W. L. Cheng, H. Li, P. E. D. Love, and Z. Irani, “Network communication in the construction industry,” Corporate Communications: An International Journal, vol. 6, no. 2, pp. 61–70, 2001.
[2] M. Schlichting, M. M. Díaz, J. Xin, and M. Rosbash, “Neuron-specific knockouts indicate the importance of network communication to Drosophila rhythmicity,” Elife, vol. 8, p. e48301, 2019.
[3] M. Kang, G. Yang, Y. Yoo, and C. Yoo, “TensorExpress: In-network communication scheduling for distributed deep learning,” in 2020 IEEE 13th international conference on cloud computing (CLOUD), 2020, pp. 25–27.
[4] X.-B. Chen, Y.-L. Wang, G. Xu, and Y.-X. Yang, “Quantum network communication with a novel discrete-time quantum walk,” Ieee Access, vol. 7, pp. 13634–13642, 2019.
[5] S. K. Tripathi, M. Kumar, and A. Kumar, "Graphene-based tunable and wideband terahertz antenna for wireless network communication," Wireless Networks, vol. 25, no. 7, pp. 4371–4381, 2019.
[6] W. Li, Z. Xie, J. Zhao, and P. K. Wong, "Velocity-based robust fault-tolerant automatic steering control of autonomous ground vehicles via adaptive event-triggered network communication," Mechanical Systems and Signal Processing, vol. 143, p. 106798, 2020.
[7] Y.-C. Sun and G.-H. Yang, “Event-triggered state estimation for networked control systems with lossy network communication,” Information Sciences, vol. 492, pp. 1–12, 2019.
[8] V. N. Poole, O.-Y. Lo, T. Wooten, I. Iloputaife, L. A. Lipsitz, and M. Esterman, “Motor-cognitive neural network communication underlies walking speed in community-dwelling older adults,” Frontiers in aging neuroscience, vol. 11, p. 159, 2019.
[9] H. Lin, Z. T. Kalbarczyk, and R. K. Iyer, "Raincoat: Randomization of network communication in power grid cyberinfrastructure to mislead attackers," IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 4893–4906, 2018.
[10] T. Kustermann, T. Popov, G. A. Miller, and B. Rockstroh, “Verbal working memory‐related neural network communication in schizophrenia,” Psychophysiology, vol. 55, no. 9, p. e13088, 2018.
[11] H. Huang et al., "Deep learning for physical-layer 5G wireless techniques: Opportunities, challenges, and solutions," IEEE Wireless Communications, vol. 27, no. 1, pp. 214–222, 2019.
[12] H. Wu, X. Li, and Y. Deng, “Deep learning-driven wireless communication for edge-cloud computing: opportunities and challenges,” Journal of Cloud Computing, vol. 9, no. 1, pp. 1–14, 2020.
[13] Z. Tao and Q. Li, “{eSGD}: Communication Efficient Distributed Deep Learning on the Edge,” 2018.
[14] G. Thamilarasu and S. Chawla, “Towards deep-learning-driven intrusion detection for the internet of things,” Sensors, vol. 19, no. 9, p. 1977, 2019.
[15] Z. Lv, A. K. Singh, and J. Li, “Deep learning for security problems in 5G heterogeneous networks,” IEEE Network, vol. 35, no. 2, pp. 67–73, 2021.
[16] A. Koloskova, T. Lin, S. U. Stich, and M. Jaggi, “Decentralized deep learning with arbitrary communication compression,” arXiv preprint arXiv:1907.09356, 2019.
[17] J. Chen, K. Li, Q. Deng, K. Li, and S. Y. Philip, “Distributed deep learning model for intelligent video surveillance systems with edge computing,” IEEE Transactions on Industrial Informatics, 2019.
[18] M. Mahdavisharif, S. Jamali, and R. Fotohi, “Big data-aware intrusion detection system in communication networks: a deep learning approach,” Journal of Grid Computing, vol. 19, no. 4, pp. 1– 28, 2021.
[19] N. Shazeer et al., "Mesh-TensorFlow: Deep learning for supercomputers," Advances in neural information processing systems, vol. 31, 2018.
[20] H. Geng, H. Liu, L. Ma, and X. Yi, "Multi-sensor filtering fusion meets censored measurements under a constrained network environment: advances, challenges, and prospects," International Journal of Systems Science, vol. 52, no. 16, pp. 3410–3436, 2021.
[21] C. Yuan, W. Yue-ping, T. Xue-mei, and Z. Xiao-fang, “An evaluation method for network communication system efficiency based on multi-source information fusion,” in 2018 37th Chinese Control Conference (CCC), 2018, pp. 4305–4309.
[22] T. Lin, P. Wu, and F. Gao, “Information security of flowmeter communication network based on multisensory data fusion,” Energy Reports, vol. 8, pp. 12643–12652, 2022.
[23] A.-M. Yang, X.-L. Yang, J.-C. Chang, B. Bai, F.-B. Kong, and Q.-B. Ran, "Research on a fusion scheme of the cellular network and wireless sensor for cyber-physical social systems," Ieee Access, vol. 6, pp. 18786–18794, 2018.
[24] T. Li, J. M. Corchado, and S. Sun, "Partial consensus and conservative fusion of Gaussian mixtures for distributed Ph.D. fusion," IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 5, pp. 2150–2163, 2018.
[25] Y. Yuan and Y. Li, “Research on Spatial Agglomeration Characteristics of Aerospace Cultural and Creative Industries in Smart City under Multidata Fusion,” Security and Communication Networks, vol. 2022, 2022.
[26] R. Xu, H. Xiang, X. Xia, X. Han, J. Li, and J. Ma, “Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communication,” in 2022 International Conference on Robotics and Automation (ICRA), 2022, pp. 2583–2589.
[27] G. Li, Z. Yan, Y. Fu, and H. Chen, “Data fusion for network intrusion detection: a review,” Security and Communication Networks, vol. 2018, 2018.
