Towards an In-Depth Detection of Malware Using Multi-QCNN

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 using only a few qubits, we implement a new preprocessing of our dataset using Grayscale method, and we couple it with a model composed of five 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.