Quantum Machine Learning in Intrusion Detection Systems: a Systematic Mapping Study

Abstract

The integration between quantum computing (QC) and machine learning algorithms (ML) aims to speed up computing processes and increase model accuracy rates, and this is what led developers and researchers to exploit this feature to study improving the performance of intrusion detection systems (IDSs). In this work, we present a systematic mapping review (SMR) of the most important works in the field of using quantum machine learning (QML) to increase the efficiency of anomaly detection techniques, which depend mainly on ML. After defining and applying the research methodology, the preliminary search results amounted to 240 studies from four databases. According to the exclusion and inclusion reports, we obtained 21 main studies. After reviewing and analyzing the results, the four research questions were answered. The review focused on the development of integration of intrusion detection systems with QML, the characteristics of such integration, increasing the efficiency of ML algorithms, and future research opportunities in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.