Algorithm Selection Framework for Cyber Attack Detection

Document Type

Conference Proceeding

Publication Date



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.


Copyright statement: © 2020 Association for Computing Machinery.

The "Link to Full Text" on this page opens the arXiv e-print hosted at the arXiv repository.

arXiv:2005.14230 [cs.CR]

Source Publication

WiseML 2020 - Proceedings of the 2nd ACM Workshop on Wireless Security and Machine Learning