Quantum Computing Methods for Malware Detection
This work addresses malware detection for cybersecurity using quantum computing, but it is incremental as it applies an existing quantum method to a new domain without demonstrating clear advantages.
The paper tackled malware detection by comparing Quantum Support Vector Machine (QSVM) to classical SVM on a dataset of PE files, finding that QSVM was implemented and evaluated on simulators and IBM quantum hardware with insights into performance and practical issues like transpilation and job size limits, but no concrete performance numbers were provided.
In this paper, we explore the potential of quantum computing in enhancing malware detection through the application of Quantum Machine Learning (QML). Our main objective is to investigate the performance of the Quantum Support Vector Machine (QSVM) algorithm compared to SVM. A publicly available dataset containing raw binaries of Portable Executable (PE) files was used for the classification. The QSVM algorithm, incorporating quantum kernels through different feature maps, was implemented and evaluated on a local simulator within the Qiskit SDK and IBM quantum computers. Experimental results from simulators and quantum hardware provide insights into the behavior and performance of quantum computers, especially in handling large-scale computations for malware detection tasks. The work summarizes the practical experience with using quantum hardware via the Qiskit interfaces. We describe in detail the critical issues encountered, as well as the fixes that had to be developed and applied to the base code of the Qiskit Machine Learning library. These issues include missing transpilation of the circuits submitted to IBM Quantum systems and exceeding the maximum job size limit due to the submission of all the circuits in one job.