Anomaly Detection Using Supervised learning Techniques in Social Networks
DOI:
https://doi.org/10.31185/wjcm.58Abstract
Intrusion detection corresponds to a suite of techniques that are used to identify attacks against computers and network infrastructures. As the cost of the information processing and Internet accessibility falls, more and more organizations are becoming vulnerable to a wide variety of cyber threats. Web mining based intrusion detection techniques generally fall into one of two categories; misuse detection and anomaly detection. In misuse detection, each instance in a data set is labelled as ‘normal’ or ‘intrusive’ and a learning algorithm is trained over the labelled data. These techniques are able to automatically retrain intrusion detection models on different input data that include new types of attacks, as long as they have been labelled appropriately. Evaluation results show that the proposed approach can reduce the number of alerts by 94.32%, effectively improving alert management process. Because of the use of ensemble approach and optimal algorithms in the proposed approach, it can inform network security specialist the state of the monitored network in an online manner.
References
Y. Xu, C. Yan, J. Shi, Z. Lu, X. Niu, Y. Jiang, and F. Zhu, “An anomaly detection and dynamic energy performance evaluation method for HVAC systems based on data mining,” Sustainable Energy Technologies and Assessments, vol. 44, pp. 101092–101092, 2021.
A. Rajesh and S. &kiran, “Anomaly Detection Using Data Mining Techniques in Social Networking,” International Journal for Research in Applied Science and Engineering Technology, vol. 6, pp. 1268–1272.
S. Jose, D. Malathi, B. Reddy, and D. &jayaseeli, “A survey on anomaly based host intrusion detection system,” Journal of Physics: Conference Series, vol. 1000, pp. 12049–12049, 2018.
M. H. Oh and G. &iyengar, “Sequential anomaly detection using inverse reinforcement learning,” Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & data mining, pp. 1480–1490, 2019.
J. Gao, X. Song, Q. Wen, P. Wang, L. Sun, and H. &xu, “Robusttad: Robust time series anomaly detection via decomposition and convolutional neural networks,” 2020.
C. Fan, F. Xiao, Y. Zhao, and J. Wang, “Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data,” Applied energy, vol. 211, pp. 1123–1135, 2018.
V. Hajisalem and S. &babaie, “A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection,” Computer Networks, vol. 136, pp. 37–50, 2018.
S. Thudumu, P. Branch, J. Jin, and J. J. Singh, “A comprehensive survey of anomaly detection techniques for high dimensional big data,” Journal of Big Data, vol. 7, no. 1, pp. 1–30, 2020.
T. Wen and R. Keyes, “Time series anomaly detection using convolutional neural networks and transfer learning,” 2019.
M. Riveiro, G. Pallotta, and M. &vespe, “Maritime anomaly detection: A review,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 5, pp. 1266–1266, 2018.
A. Guezzaz, Y. Asimi, M. Azrour, and A. &asimi, “Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection,” Big Data Mining and Analytics, vol. 4, no. 1, pp. 18–24, 2021.
L. Basora, X. Olive, and T. &dubot, “Recent advances in anomaly detection methods applied to aviation,” Aerospace, vol. 6, no. 11, pp. 117– 117, 2019.
A. Dogan and D. &birant, “Machine learning and data mining in manufacturing,” Expert Systems with Applications, vol. 166, pp. 114060– 114060, 2021.
T. Nolle, A. Seeliger, and M. &mühlhäuser, “BINet: multivariate business process anomaly detection using deep learning,” in International Conference on Business Process Management, pp. 271–287, Springer, 2018.
R. K. Pandit and D. Infield, “SCADA-based wind turbine anomaly detection using Gaussian process models for wind turbine condition monitoring purposes,” IET Renewable Power Generation, vol. 12, no. 11, pp. 1249–1255, 2018.
L. Erhan, M. Ndubuaku, M. D. Mauro, W. Song, M. Chen, G. Fortino, . . &liotta, and A, “Smart anomaly detection in sensor systems: A multi-perspective review,” Information Fusion, vol. 67, pp. 64–79, 2021.
A. Capozzoli, M. S. Piscitelli, S. Brandi, D. Grassi, and G. &chicco, “Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings,” Energy, vol. 157, pp. 336–352, 2018.
M. Munir, S. A. Siddiqui, A. Dengel, and S. Ahmed, “DeepAnT: A deep learning approach for unsupervised anomaly detection in time series,” Ieee Access, vol. 7, 1991.
L. Feremans, V. Vercruyssen, B. Cule, W. Meert, and B. Goethals, “Pattern-based anomaly detection in mixed-type time series,” in Joint European conference on machine learning and knowledge discovery in databases, pp. 240–256, Springer, 2019.
P. J. Rousseeuw and M. Hubert, “Anomaly detection by robust statistics,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol. 8, no. 2, pp. 1236–1236, 2018.
M. Idhammad, K. Afdel, and M. &belouch, “Distributed intrusion detection system for cloud environments based on data mining techniques,” Procedia Computer Science, vol. 127, pp. 35–41, 2018.
E. Stripling, B. Baesens, B. Chizi, and S. &vandenbroucke Isolation-based conditional anomaly detection on mixed-attribute data to uncover workers’ compensation fraud. Decision Support Systems, vol. 111, pp. 13–26, 2018.
A. Chaudhary, H. Mittal, and A. Arora, “Anomaly detection using graph neural networks,” 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), pp. 346–350, 2019.
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