QML-IDS: Quantum Machine Learning Intrusion Detection System
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913573076205568 |
|---|---|
| author | Abreu, Diego Rothenberg, Christian Esteve Abelem, Antonio |
| author_facet | Abreu, Diego Rothenberg, Christian Esteve Abelem, Antonio |
| contents | The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational power paves the way for creating strategies to mitigate the constantly advancing threats to network integrity. In response to this technological advancement, our research presents QML-IDS, a novel Intrusion Detection System~(IDS) that combines quantum and classical computing techniques. QML-IDS employs Quantum Machine Learning~(QML) methodologies to analyze network patterns and detect attack activities. Through extensive experimental tests on publicly available datasets, we show that QML-IDS is effective at attack detection and performs well in binary and multiclass classification tasks. Our findings reveal that QML-IDS outperforms classical Machine Learning methods, demonstrating the promise of quantum-enhanced cybersecurity solutions for the age of quantum utility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16308 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | QML-IDS: Quantum Machine Learning Intrusion Detection System Abreu, Diego Rothenberg, Christian Esteve Abelem, Antonio Cryptography and Security The emergence of quantum computing and related technologies presents opportunities for enhancing network security. The transition towards quantum computational power paves the way for creating strategies to mitigate the constantly advancing threats to network integrity. In response to this technological advancement, our research presents QML-IDS, a novel Intrusion Detection System~(IDS) that combines quantum and classical computing techniques. QML-IDS employs Quantum Machine Learning~(QML) methodologies to analyze network patterns and detect attack activities. Through extensive experimental tests on publicly available datasets, we show that QML-IDS is effective at attack detection and performs well in binary and multiclass classification tasks. Our findings reveal that QML-IDS outperforms classical Machine Learning methods, demonstrating the promise of quantum-enhanced cybersecurity solutions for the age of quantum utility. |
| title | QML-IDS: Quantum Machine Learning Intrusion Detection System |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2410.16308 |