Applying QMLP and QCNN quantum classifiers to five malware datasets yields binary accuracies up to 96% and multiclass accuracy up to 95.7%, with QMLP generally more accurate and QCNN faster.
Towards an in-depth detection of malware using distributed QCNN
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abstract
Malware detection is an important topic of current cybersecurity, and Machine Learning appears to be one of the main considered solutions even if certain problems to generalize to new malware remain. In the aim of exploring the potential of quantum machine learning on this domain, our previous work showed that quantum neural networks do not perform well on image-based malware detection when using a few qubits. In order to enhance the performances of our quantum algorithms for malware detection using images, without increasing the resources needed in terms of qubits, we implement a new preprocessing of our dataset using Grayscale method, and we couple it with a model composed of five distributed quantum convolutional networks and a scoring function. We get an increase of around 20 \% of our results, both on the accuracy of the test and its F1-score.
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Towards Quantum Machine Learning for Malicious Code Analysis
Applying QMLP and QCNN quantum classifiers to five malware datasets yields binary accuracies up to 96% and multiclass accuracy up to 95.7%, with QMLP generally more accurate and QCNN faster.