PHM-Based Modeling for Cyberattack Classifier Performance

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Published Nov 11, 2024
Priscila Silva

Abstract

This research implements Prognostics and Health Management (PHM) using multiple linear regression and multivariate time series models to monitor and predict when the performance of a Machine Learning-based cyberattack classifier might degrade to an unacceptable level, enabling preemptive maintenance strategies.

How to Cite

Silva, P. (2024). PHM-Based Modeling for Cyberattack Classifier Performance. Annual Conference of the PHM Society, 16(1). https://doi.org/10.36001/phmconf.2024.v16i1.4177
Abstract 59 | PDF Downloads 36

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Keywords

Prognostics and Health Management, Machine Learning Algorithms, Cyberattack Classifier, Multiple Linear Regression, Multivariate Time Series

References
Ahmad, Z., Khan, A. S., Shiang, C. W., Abdullah, J., & Ahmad, F. (2020). Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies, 32, e4150.
Baye, G., Silva, P., Broggi, A., Fiondella, L., Bastian, N. D., & Kul, G. (2023). Performance analysis of deeplearning based open set recognition algorithms for network intrusion detection systems. In Noms 2023-2023 ieee/ifip network operations and management symposium (p. 1-6).
Brandt, P., &Williams, J. (2007). Multiple time series models (No. no. 148). SAGE Publications.
Javaid, A., Niyaz, Q., Sun, W., & Alam, M. (2016). A deep learning approach for network intrusion detection system. In Proceedings of the 9th eai international conference on bio-inspired information and communications technologies (formerly bionetics) (pp. 21–26).
Kleinbaum, D. G., Kupper, L. L., Nizam, A., & Rosenberg, E. S. (1999). Applied regression analysis and other multivariable methods. Cengage Learning.
Lewis, J. A. (2002). Assessing the risks of cyber terrorism, cyber war and other cyber threats. Center for Strategic & International Studies Washington, DC.
Liao, H.-J., Richard Lin, C.-H., Lin, Y.-C., & Tung, K.-Y. (2013). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications, 36(1), 16-24.
Lo, W. W., Layeghy, S., Sarhan, M., Gallagher, M., & Portmann, M. (2022). E-graphsage: A graph neural network based intrusion detection system for iot. In Noms 2022-2022 ieee/ifip network operations and management symposium (p. 1-9).
Narayana Rao, K., Venkata Rao, K.,&P.V.G.D., P. R. (2021). A hybrid intrusion detection system based on sparse autoencoder and deep neural network. Computer Communications, 180, 77-88.
Pham, H. (1999). Software reliability. John Wiley & Sons.
Rosay, A., Cheval, E., Carlier, F., & Leroux, P. (2022). Network intrusion detection: A comprehensive analysis of cic-ids2017. In 8th international conference on information systems security and privacy (pp. 25–36).
Sharma, J., Giri, C., Granmo, O.-C., & et al. (2019). Multilayer intrusion detection system with extratrees feature selection, extreme learning machine ensemble, and softmax aggregation. EURASIP Journal on Information Security, 2019(15). doi: 10.1186/s13635-019-0098-y
Wu, H., Sun, P., Liu, P., Li, Q., Liu, C., Lu, X., . . . Chen, J. (2020). Dl-ids: Extracting features using cnn-lstm hybrid network for intrusion detection system. Security and Communication Networks, 2020, 8890306.
Section
Doctoral Symposium Summaries