A Novel Method for Cyber Threat Detection Based on Sliding Window Approach
Keywords:
Fault Detection, Sliding Window, Big Data, Streaming DataAbstract
In industrial automation, high-dimensional data streams have become more common. The system's dependability and stability can be ensured if system defects can be detected efficiently using this data. The "curse of dimensionality" and conception wandering are the two fundamental problems induced by increased dimensionality and digital data, and one ongoing goal is to handle them both at the same time. This work proposes a method for detecting faults in non - stationarity higher dimensional streaming data. To discover low-dimensional subdomain defects from high-dimensional datasets, anomaly detection technique with subspace mechanism tends to be presented.It calculates the variance inflation of an entity in its subdomain translation and identifies fault tolerant categories by assessing different angles. The technique is then developed to an online application that can continually monitor model parameters depending on the feature extraction concept. On synthetic datasets, we implemented the accuracy technique to local outlier factor-based ways to evaluate it, and discovered that the technique was more accurate. The research's findings showed the proposed computation effectiveness. Researchers further claimed that the method can detect low impact complexity defects those baseline characteristics in high dimensional complexity and it can adapt to analyzed system's timely behaviour. The major contribution of this research is an operational subspace learning technique for defect identification.
Downloads
References
Chandola, V.; Banerjee, A.; Kumar, V. Anomaly detection: A survey. ACM Comput. Surv. 2009, 41, 1–58.
Gupta, M.; Gao, J.; Aggarwal, C.C.; Han, J. Outlier detection for temporal data: A survey. IEEE Trans. Knowl. Data Eng. 2013, 26, 2250–2267.
Ahmad, S.; Purdy, S. Real-time anomaly detection for streaming analytics. arXiv 2016, arXiv:1607.02480.
Thakkar, P.; Vala, J.; Prajapati, V. Survey on outlier detection in data stream. Int. J. Comput. Appl. 2016, 136, 13–16.
Mary Mathew, R. ., & Gunasundari, R. . (2023). An Oversampling Mechanism for Multimajority Datasets using SMOTE and Darwinian Particle Swarm Optimisation. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 143–153. https://doi.org/10.17762/ijritcc.v11i2.6139
Mishra, S.; Chawla, M. A comparative study of local outlier factor algorithms for outliers detection in data streams. In Emerging Technologies in Data Mining and Information Security; Springer: Singapore, 2019; pp. 347–356.
Park, C.H. Outlier and anomaly pattern detection on data streams. J. Supercomput. 2019, 75, 6118–6128.
Zhang, M.; Guo, J.; Li, X.; Jin, R. Data-Driven Anomaly Detection Approach for Time-Series Streaming Data. Sensors 2020,20, 5646.
Alghushairy, O.; Alsini, R.; Soule, T.; Ma, X. A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams. Big Data Cogn. Comput. 2021, 5, 1.
Braei, M.; Wagner, S. Anomaly detection in univariate time-series: A survey on the state-of-the-art. arXiv 2020, arXiv:2004.00433.
Vyas, A. ., & Sharma, D. A. . (2020). Deep Learning-Based Mango Leaf Detection by Pre-Processing and Segmentation Techniques. Research Journal of Computer Systems and Engineering, 1(1), 11–16. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/18
Gao, C.; Chen, Y.; Wang, Z.; Xia, H.; Lv, N. Anomaly detection frameworks for outlier and pattern anomaly of time series in wireless sensor networks. In Proceedings of the 2020 International Conference on Networking and Network Applications (NaNA), Haikou, China, 10–13 December 2020; pp. 229–232.
Safaei, M.; Asadi, S.; Driss, M.; Boulila, W.; Alsaeedi, A.; Chizari, H.; Abdullah, R.; Safaei, M. A systematic literature review on outlier detection in wireless sensor networks. Symmetry 2020, 12, 328.
Blázquez-García, A.; Conde, A.; Mori, U.; Lozano, J.A. A Review on Outlier/Anomaly Detection in Time Series Data. ACM Comput. Surv. 2021, 54, 1–33.
Rousseeuw, P.J.; Croux, C. Alternatives to the median absolute deviation. J. Am. Stat. Assoc. 1993, 88, 1273–1283.
Leys, C.; Ley, C.; Klein, O.; Bernard, P.; Licata, L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 2013, 49, 764–766.
Hochenbaum, J.; Vallis, O.S.; Kejariwal, A. Automatic anomaly detection in the cloud via statistical learning. arXiv 2017, arXiv:1704.07706.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Soumya T. R., S. Revathy
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
