Presenter(s)
Kayla D Chisholm
Files
Download Project (241 KB)
Description
Over the last two years, machine learning has become rapidly utilized in cybersecurity, rising from almost non-existent to currently over half of cybersecurity techniques utilized commercially. Machine learning is advancing at a rapid rate, and the application of new learning techniques to cybersecurity have yet to be investigated. The presented work discusses the use of novel machine learning algorithms for network intrusion detection. Typically, this type of network intrusion detection system operates based on a set of rules programmed to recognize known attacks and intrusion techniques. However, this detection method does little to prevent new, or ‘zero day’ attacks. On the other hand, a detection system that uses machine learning could analyze patterns in network data in real time to determine attack likelihood. To test the efficacy of machine learning and neural network algorithms for network security, we use publicly available data sets to ‘teach’ these neural systems what an attack may look like. After training, the system will be tested to determine how well it learned the features contained within the input data. Our results show accuracy and error rates of the algorithms that have been implemented.
Publication Date
4-24-2019
Project Designation
Honors Thesis
Primary Advisor
Chris G. Yakopcic
Primary Advisor's Department
Electrical and Computer Engineering
Keywords
Stander Symposium project
Recommended Citation
"Machine Learning for Cyberattack Detection" (2019). Stander Symposium Projects. 1695.
https://ecommons.udayton.edu/stander_posters/1695
![Machine Learning for Cyberattack Detection](https://ecommons.udayton.edu/stander_posters/2695/thumbnail.jpg)