Tensor Networks for Explainable Machine Learning in Cybersecurity

arXiv:2401.00867v3 Announce Type: replace
Abstract: In this paper we show how tensor networks help in developing explainability of machine learning algorithms. Specifically, we develop an unsupervised clustering algorithm based on Matrix Product States (MPS) and apply it in the context of a real use-case of adversary-generated threat intelligence. Our investigation proves that MPS rival traditional deep learning models such as autoencoders and GANs in terms of performance, while providing much richer model interpretability. Our approach naturally facilitates the extraction of …

abstract algorithm algorithms apply arxiv case clustering clustering algorithm context cs.ai cs.lg cybersecurity explainability explainable machine learning generated intelligence investigation machine machine learning machine learning algorithms matrix networks paper product quant-ph show tensor threat threat intelligence type unsupervised