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In today’s rapidly evolving technology era, cybersecurity threats have become sophisticated, challenging conventional detection and defense. Classical machine learning aids early threat detection but lacks real-time data processing and adaptive threat detection due to the reliance on large, clean datasets. New attack techniques emerge daily, and data scale and complexity limit classical computing. Quantum-based machine learning (QML) using quantum computing (QC) offers solutions. QML combines QC and machine learning to analyze big data effectively. This paper investigates multiple QML algorithms and compares their performance with their classical counterparts.
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Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?
Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.
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- PAR ID:
- 10347034
- Date Published:
- Journal Name:
- IEEE Conference on Computers, Software & Applications
- Page Range / eLocation ID:
- 530-538
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1109/COMPSAC54236.2022.00097
