Tensor Networks for Explainable Machine Learning in Cybersecurity
This addresses the need for transparency in AI decisions for cybersecurity applications, though it appears incremental as it applies an existing tensor network method to a new domain.
The paper tackled the problem of explainable machine learning in cybersecurity by developing an unsupervised clustering algorithm based on Matrix Product States (MPS) for adversary-generated threat intelligence, showing that MPS rival traditional deep learning models like autoencoders and GANs in performance while providing richer interpretability.
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 feature-wise probabilities, Von Neumann Entropy, and mutual information, offering a compelling narrative for classification of anomalies and fostering an unprecedented level of transparency and interpretability, something fundamental to understand the rationale behind artificial intelligence decisions.