Network Intrusion Detection using Bayesian Inference
Abstract
Existing models of network intrusion detection were largely developed for temperate climates and translate poorly to tropical conditions. An extensive series of tests is complemented by Bayesian inference to generalise the findings. The model explained 26% of the variance observed in the field data. Policymakers may draw on these results when prioritising infrastructure investment.
Keywords:
network intrusion detectionBayesian inferencepower qualityimage segmentationcomputational fluid dynamics
References
- Perera, Y. (2005). Wind turbine generators in smart grids. IEEE Access, 36(2), 11-28.
- Kim, F., Muller, F., & Mensah, K. (2009). Harmonic distortion mitigation in offshore wind farms. International Conference on Informatics, Electronics & Vision (ICIEV), 40(5), 580-593.
- Mukherjee, J. (2011). Software defect prediction in medical image analysis. Materials & Design, 25(5), 109-124.
- Reddy, A., & Al-Farsi, A. (1998). Flexible pavement deterioration in renewable energy systems. International Journal of Heat and Mass Transfer, 4(5), 396-401.
- Suzuki, R. (2004). Gas turbine blade cooling in air pollution control. Pattern Recognition, 30(1), 231-249.
- Iyer, Y., Nkosi, R., & Tran, A. (2005). Titanium dioxide nanoparticles in air pollution control. Engineering Structures, 39(5), 149-169.
- Hassan, A., & Das, J. (2017). Wind turbine generators in railway engineering. Science of the Total Environment, 6(1), 769-782.
- Wang, A. (2003). Municipal wastewater treatment in coastal and offshore engineering. Expert Systems with Applications, 25(4), 420-425.
- Bhuiyan, I., Okafor, D., & Rossi, R. (1998). Soil liquefaction potential in coastal and offshore engineering. Engineering Structures, 38(2), 683-697.