Algorithm selection framework for cyber attack detection
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Authors
Chalé, Marc
Bastian, Nathaniel D.
Weir, Jeffery
Issue Date
2020-07
Type
proceedings-article
Language
en_US
Keywords
Alternative Title
Abstract
The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the taxonomy. Both strategies recommend a high-performing algorithm, though not the best performing. The work demonstrates the close connectedness between algorithm selection and the taxonomy for which it is premised.
Description
Citation
Chalé, M., Bastian, N. D., & Weir, J. D. (2020). Algorithm selection framework for cyber attack detection. https://doi.org/10.1145/3395352.3402623