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.

翻译：恶意软件检测是当前网络安全领域的重要课题，机器学习作为主要解决方案之一，仍面临难以泛化检测新型恶意软件的问题。为探索量子机器学习在该领域的潜力，我们此前的研究表明，在使用少量量子比特时，量子神经网络对基于图像的恶意软件检测效果不佳。为了在不增加量子比特资源需求的前提下提升用于图像恶意软件检测的量子算法性能，我们采用灰度法对数据集进行全新预处理，并将其与由五个分布式量子卷积网络及评分函数组成的模型相结合。结果显示，检测准确率与F1分数均提升了约20%。