Abstract
In this chapter, we present a comparative study on the performance of Neural Network training algorithms towards the goal of developing an intelligent system that can classify, in real-time, the behavior of control systems. An investigation on the performance of five neural network training algorithms: Levenberg–Marquardt, Broyden–Fletcher–Goldfarb–Shanno (BFGS) Quasi–Newton, Resilient Backpropagation, Scaled Conjugate Gradient, and Gradient Descent with Momentum and Adaptive Learning Rate, in classifying 30,000 records of simulated operational data on a typical industrial control system is conducted. The comparisons are made on four neural network system metrics: network error performance, success rate, run time, and number of epochs (iterations). The results are tabulated and analyzed. The chapter concludes with perceptive observations and offers avenues for future research extensions. We envision this small scale study would pave the way to the utilization of intelligent analytics as an avenue towards the realization of an enhanced security posture of our nation’s critical infrastructures. Further, this case study on the application of machine learning technology on information security may offer additional forum for academic inquisition.
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Acknowledgement
This work is supported in part by a Center for Academic Excellence (CAE) Cyber Security Research Program grant (Grant Award Number H98230-15-1-0270) from the National Security Agency (NSA). Opinions expressed are those of the authors and not necessarily of the granting agency.
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Kim, J., Francia, G. (2018). A Comparative Study of Neural Network Training Algorithms for the Intelligent Security Monitoring of Industrial Control Systems. In: Daimi, K. (eds) Computer and Network Security Essentials. Springer, Cham. https://doi.org/10.1007/978-3-319-58424-9_30
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DOI: https://doi.org/10.1007/978-3-319-58424-9_30
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