Volume 12 , Issue 2 , PP: 36-51`, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Pallavi Goel 1 * , Sarika Chaudhary 2
Doi: https://doi.org/10.54216/JCIM.120203
The paper discusses major components of the proposed intrusion detection system as well as associated ideas. Dimensionality reduction solutions are highly valued for their potential to improve the efficiency of anomaly detection. Furthermore, feature selection and fusion methods are applied to optimise the system's capabilities. The following summary of network control, management, and cloud-based network processing aspects highlights operations managers, cloud resources, network function virtualization (NFV), and hardware and software components. We discuss prospective Deep Autoencoders (DAEs) applications, such as their use in the dimensionality reduction module, training methodologies, and benefits. Data transformation utilising coded representations is also graphically displayed and described in the text using an encoder and decoder system. The role of the anomaly detection via virtual network function in the suggested technique is also investigated. This component leverages a deep neural network (DNN) to identify anomalies in the 5G network's peripherals. DNN design issues, optimisation methodologies, and the trade-off between model complexity and detection efficacy are also discussed. Overall, the passage provides an overview of the proposed intrusion detection scheme, its components, and the techniques employed, underscoring their contributions to improving efficiency, accuracy, and security in Next Generation Networks.
5G networks , Anomaly Detection , Deep Learning , Dimensionality Reduction , Intrusion Detection , Network Function Virtualization.
[1] Cid-Fuentes, J. A., Szabo, C., & Falkner, K. (2018). Adaptive performance anomaly detection in distributed systems using online SVMS. IEEE Transactions on Dependable and Secure Computing, 17(5), 928-941.
[2] Borghesi, A., Bartolini, A., Lombardi, M., Milano, M., & Benini, L. (2019). A semi-supervised autoencoder-based approach for anomaly detection in high-performance computing systems. Engineering Applications of Artificial Intelligence, 85, 634-644.
[3] Zhu, M., Ye, K., & Xu, C. Z. (2018). Network anomaly detection and identification based on deep learning methods. In International Conference on Cloud Computing (pp. 219-234). Springer.
[4] Siffer, A., Fouque, P. A., Termier, A., & 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 (pp. 1067-1075). Association for Computing Machinery.
[5] Hu, M., Ji, Z., Yan, K., Guo, Y., Feng, X., Gong, J., ... Dong, L. (2018). Detecting anomalies in time series data via a meta-feature based approach. IEEE Access, 6, 27760-27776.
[6] Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (pp. 93-104). Association for Computing Machinery.
[7] Audibert, J., Michiardi, P., Guyard, F., Marti, S., & Zuluaga, M. A. (2020). USAD: Unsupervised anomaly detection on multivariate time series. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3395-3404).
[8] Zhang, H., Yu, Y., Jiao, J., Xing, E., El Ghaoui, L., & Jordan, M. (2019). Theoretically principled trade-off between robustness and accuracy. In Proceedings of the 36th International Conference on Machine Learning (ICML), Vol. 97 of Proceedings of Machine Learning Research, PMLR (pp. 7472-7482).
[9] Al-Khasawneh, M. A., Uddin, I., Shah, S. A. A., et al. (2022). An Improved Chaotic Image Encryption Algorithm using Hadoop-based MapReduce framework for massive remote sensed images in parallel IoT applications. Cluster Computing, 25(2), 999-1013. doi: 10.1007/s10586-021-03466-2
[10] Parashar, V., Kashyap, R., Rizwan, A., Karras, D. A., Altamirano, G. C., Dixit, E., & Ahmadi, F. (2022). Aggregation-based dynamic channel bonding to maximise the performance of wireless local area networks (WLAN). Wireless Communications and Mobile Computing, 2022, 1–11. https://doi.org/10.1155/2022/4464447
[11] Nair, R., Vishwakarma, S., Soni, M., Patel, T., & Joshi, S. (2021). Detection of covid-19 cases through X-ray images using hybrid deep neural network. World Journal of Engineering, 19(1), 33–39.
[12] Wu, W., He, L., Lin, W., Su, Y., Cui, Y., Maple, C., & Jarvis, S. A. (2020). Developing an unsupervised real-time anomaly detection scheme for time series with multi-seasonality. IEEE Transactions on Knowledge and Data Engineering, 34(9), 4147-4160.
[13] Ibidunmoye, O., Hernández-Rodriguez, F., & Elmroth, E. (2015). Performance anomaly detection and bottleneck identification. ACM Computing Surveys (CSUR), 48(1), 1-35.
[14] Qi, G. J., & Luo, J. (2020). Small data challenges in the big data era: A survey of recent progress on unsupervised and semi-supervised methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(4), 2168-2187.
[15] Su, Y., Zhao, Y., Niu, C., Liu, R., Sun, W., & 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 and Data Mining (pp. 2828-2837). Association for Computing Machinery.
[16] Tang, J., Chen, Z., Fu, A. W. C., & Cheung, D. W. (2002). Enhancing effectiveness of outlier detections for low-density patterns. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 535-548). Springer.
[17] Kashyap, R. (2021). Systematic model for decision support system. In Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering (pp. 78–106). Retrieved from https://doi.org/10.4018/978-1-7998-9023-2.ch004
[18] Nair, R., Singh, D. K., Ashu, Yadav, S., & Bakshi, S. (2020). Hand gesture recognition system for physically challenged people using IOT. In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS).
[19] Khan, Z. A., Feng, Z., Uddin, M. I., Mast, N., Shah, S. A. A., Imtiaz, M., Al-Khasawneh, M. A., & Mahmoud, M. (2020). Optimal Policy Learning for Disease Prevention Using Reinforcement Learning. Scientific Programming, 2020, Article ID 7627290, 1-13. doi: 10.1155/2020/7627290
[20] Papadimitriou, S., Kitagawa, H., Gibbons, P. B., & Faloutsos, C. (2003). LOCI: Fast outlier detection using the local correlation integral. In Proceedings of the 19th International Conference on Data Engineering (pp. 315-326). IEEE.
[21] Ramaswamy, S., Rastogi, R., & Shim, K. (2000). Efficient algorithms for mining outliers from large datasets. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data (pp. 427-438). Association for Computing Machinery.
[22] Zhang, K., Hutter, M., & Jin, H. (2009). A new local distance-based outlier detection approach for scattered real-world data. In Pacific-Asia Conference on Knowledge Discovery and Data Mining. Lecture Notes in Computer Science, 5476, 813-822. Springer.
[23] Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443-1471.
[24] Song, Q., Hu, W., & Xie, W. (2002). Robust support vector machine with bullet hole image classification. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 32(4), 440-448.
[25] Liu, F. T., Ting, K. M., & Zhou, Z. H. (2008). Isolation forest. In 2008 Eighth IEEE International Conference on Data Mining (pp. 413-422). IEEE.
[26] Ramirez-Asis, E., Bolivar, R. P., Gonzales, L. A., Chaudhury, S., Kashyap, R., Alsanie, W. F., & Viju, G. K. (2022). A lightweight hybrid dilated ghost model-based approach for the prognosis of breast cancer. Computational Intelligence and Neuroscience, 2022, 1–10.
[27] Mohanakurup, V., Parambil Gangadharan, S. M., Goel, P., Verma, D., Alshehri, S., Kashyap, R., & Malakhil, B. (2022). Breast cancer detection on histopathological images using a composite dilated Backbone Network. Computational Intelligence and Neuroscience, 2022, 1–10.
[28] Nair, R., Alhudhaif, A., Koundal, D., Doewes, R. I., & Sharma, P. (2021). Deep learning-based COVID-19 detection system using pulmonary CT scans. Turkish Journal of Electrical Engineering & Computer Sciences, 29(SI-1), 2716–2727.
[29] Shah, S. A. A., Uddin, I., Aziz, F., Ahmad, S., Al-Khasawneh, M. A., & Sharaf, M. (2020). An Enhanced Deep Neural Network for Predicting Workplace Absenteeism. Complexity, 2020, Article ID 5843932, 1-12. doi: 10.1155/2020/5843932.
[30] Uddin, M. I., Shah, S. A. A., & Al-Khasawneh, M. A. (2020). A Novel Deep Convolutional Neural Network Model to Monitor People following Guidelines to Avoid COVID-19. Journal of Sensors, 2020, Article ID 8856801, 1-15. doi: 10.1155/2020/8856801.
[31] Kashyap, R. (2022a). Big Data and Global Software Engineering. In Research Anthology on Big Data Analytics, Architectures, and Applications (pp. 1249–1274). Retrieved from https://doi.org/10.4018/978-1-6684-3662-2.ch060.
