Algorithm selection framework for cyber attack detection

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. CCS CONCEPTS• Networks → Packet classification; • Security and privacy → Mobile and wireless security; Denial-of-service attacks.

Meta-Learner-Based Approach for Detecting Attacks on Internet of Things Networks

“…In a separate study, Chalé et al [7] introduced an intrusion detection framework rooted in meta-learning. This framework synergizes user input and data element attributes to determine the optimal algorithm for identifying cyberattacks.…”