用于网络安全中独立于平台的 Ddos 攻击分类的量子神经网络模型

IF 4.4 Q1 OPTICS
Muhammed Yusuf Küçükkara, Furkan Atban, Cüneyt Bayılmış
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引用次数: 0

摘要

量子机器学习(QML)利用量子计算的变革能力,探索包括优化、数据分析和复杂问题解决在内的广泛应用。这项研究的核心是利用量子机器学习(QML)模型开发一种创新的入侵检测系统,在分类任务中优先采用量子神经网络(QNN)架构。本研究阐明了量子神经网络的固有优势,特别是量子计算机促进的并行处理能力以及量子叠加和并行性的利用。这些特性使 QNN 能够快速高效地执行某些分类任务。利用 CIC-DDoS 2019 数据集的一个子集,通过部署和测试基于 QNN 的入侵检测系统,进行了经验验证。值得注意的是,尽管采用了较少的特征集,基于 QNN 的系统仍表现出了出色的分类准确性,达到了 92.63% 的值得称赞的比率。此外,研究还提倡利用 Qiskit 等量子计算库,以便在本地机器或量子模拟器上进行 QNN 训练。研究结果凸显了基于 QNN 的入侵检测系统在面对大规模训练数据集时获得卓越分类准确性的功效。不过,必须承认本地机器和模拟器上的量子比特数量有限所带来的限制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Quantum-Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security

Quantum-Neural Network Model for Platform Independent Ddos Attack Classification in Cyber Security

Quantum Machine Learning (QML) leverages the transformative power of quantum computing to explore a broad range of applications, including optimization, data analysis, and complex problem-solving. Central to this study is the using of an innovative intrusion detection system leveraging QML models, with a preference for Quantum Neural Network (QNN) architectures for classification tasks. The inherent advantages of QNNs, notably their parallel processing capabilities facilitated by quantum computers and the exploitation of quantum superposition and parallelism, are elucidated. These attributes empower QNNs to execute certain classification tasks expediently and with heightened efficiency. Empirical validation is conducted through the deployment and testing of a QNN-based intrusion detection system, employing a subset of the CIC-DDoS 2019 dataset. Notably, despite employing a reduced feature set, the QNN-based system exhibits remarkable classification accuracy, achieving a commendable rate of 92.63%. Moreover, the study advocates for the utilization of quantum computing libraries such as Qiskit, facilitating QNN training on local machines or quantum simulators. The findings underscore the efficacy of a QNN-based intrusion detection system in attaining superior classification accuracy when confronted with large-scale training datasets. However, it is imperative to acknowledge the constraints imposed by the limited number of qubits available on local machines and simulators.

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CiteScore
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