Automated detection of vehicles with machine learning
Document Type
Conference Proceeding
Publisher
Security Research Institute, Edith Cowan University
Faculty
Faculty of Health, Engineering and Science
School
School of Computer and Security Science / ECU Security Research Institute
RAS ID
17040
Abstract
Considering the significant volume of data generated by sensor systems and network hardware which is required to be analysed and interpreted by security analysts, the potential for human error is significant. This error can lead to consequent harm for some systems in the event of an adverse event not being detected. In this paper we compare two machine learning algorithms that can assist in supporting the security function effectively and present results that can be used to select the best algorithm for a specific domain. It is suggested that a naïve Bayesian classifier (NBC) and an artificial neural network (ANN) are most likely the best candidate algorithms for the proposed application. It was found that the NBC was faster and more accurate than the ANN for the given data set. Future research will look to repeat this process for cyber security specific applications, and also examine GPGPU optimisations to the machine learning algorithms..
DOI
10.4225/75/57b65924343cd
Access Rights
free_to_read
Comments
Johnstone, M. N., & Woodward, A. J. (2013). Automated detection of vehicles with machine learning. In Proceedings of the 11th Australian Information Security Management Conference, Edith Cowan University, Perth, Western Australia, 2nd-4th December, 2013 (pp. 42-48). (pp. 102-112). Perth, Australia: SRI-ECU. Available here