QML-IDS: Quantum Machine Learning Intrusion Detection System

• URL: http://arxiv.org/abs/2410.16308v1
• Date: Mon, 07 Oct 2024 13:07:41 GMT
• Title: QML-IDS: Quantum Machine Learning Intrusion Detection System
• Authors: Diego Abreu, Christian Esteve Rothenberg, Antonio Abelem,
• Abstract summary: We present QML-IDS, a novel Intrusion Detection System that combines quantum and classical computing techniques. QML-IDS employs Quantum Machine Learning(QML) methodologies to analyze network patterns and detect attack activities. We show that QML-IDS is effective at attack detection and performs well in binary and multiclass classification tasks.
• Score: 1.2016264781280588
• License:
• Abstract: 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.

Related papers

• Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems [42.184783937646806]
  We investigate the potential impact of quantum computing and machine learning (QML) on cybersecurity applications of traditional ML. First, we explore the potential advantages of quantum computing in machine learning problems specifically related to cybersecurity. Then, we describe a methodology to quantify the future impact of fault-tolerant QML algorithms on real-world problems.
  arXiv  Detail & Related papers  (2025-02-16T15:49:25Z)
• Quantum Bayesian Networks for Machine Learning in Oil-Spill Detection [3.9554540293311864]
  This paper introduces a novel Bayesian approach using Quantum Bayesian Networks (QBNs) to classify imbalanced datasets. We effectively address the challenge of integrating quantum enhancements with classical machine learning architectures. Our study demonstrates significant advances in detecting and classifying anomalies, contributing to more effective and precise environmental monitoring and management.
  arXiv  Detail & Related papers  (2024-12-24T15:44:26Z)
• Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning [54.80832749095356]
  Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning. This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
  arXiv  Detail & Related papers  (2024-11-14T12:27:50Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Exploring Quantum-Enhanced Machine Learning for Computer Vision: Applications and Insights on Noisy Intermediate-Scale Quantum Devices [0.0]
  This study explores the intersection of quantum computing and Machine Learning (ML) It evaluates the effectiveness of hybrid quantum-classical algorithms, such as the data re-uploading scheme and the patch Generative Adversarial Networks (GAN) model, on small-scale quantum devices.
  arXiv  Detail & Related papers  (2024-04-01T20:55:03Z)
• Predominant Aspects on Security for Quantum Machine Learning: Literature Review [0.0]
  Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning. This paper discusses the question which security concerns and strengths are connected to QML by means of a systematic literature review.
  arXiv  Detail & Related papers  (2024-01-15T15:35:43Z)
• Towards quantum enhanced adversarial robustness in machine learning [18.663564729815615]
  Integration of machine learning with quantum computing could yield better accuracy and computational efficiency. Recent work has employed quantum mechanical phenomena to defend against adversarial attacks. Despite promising early results, there remain challenges towards building robust real-world QAML tools.
  arXiv  Detail & Related papers  (2023-06-22T06:21:45Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Quantum Federated Learning with Quantum Data [87.49715898878858]
  Quantum machine learning (QML) has emerged as a promising field that leans on the developments in quantum computing to explore large complex machine learning problems. This paper proposes the first fully quantum federated learning framework that can operate over quantum data and, thus, share the learning of quantum circuit parameters in a decentralized manner.
  arXiv  Detail & Related papers  (2021-05-30T12:19:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.