New Hyperbolic Cross-Attention Model Enhances Multimodal Malware Classification

Global: New Hyperbolic Cross-Attention Model Enhances Multimodal Malware Classification

Researchers led by Nitin Choudhury and colleagues posted a paper on arXiv on 25 January 2026 describing FOCA, a multimodal framework that classifies malware by jointly analyzing audio and visual representations derived from raw binaries. The work targets the growing need for more accurate detection methods in the cybersecurity field and proposes a novel use of hyperbolic geometry to capture hierarchical relationships between modalities.

Hyperbolic Representation of Multimodal Data

The authors first convert binary files into audio waveforms and visual images, then embed each modality in Euclidean space before projecting the embeddings onto the Poincaré ball. This hyperbolic projection is intended to preserve latent hierarchical structures that Euclidean spaces may overlook, according to the paper.

Cross-Attention Mechanism in Curved Space

FOCA introduces a curvature‑aware cross‑attention layer that aligns audio and visual features while respecting the geometry of the hyperbolic space. The mechanism leverages Mobius addition to fuse the attended representations, enabling the model to integrate multimodal cues without flattening the underlying structure.

Experimental Evaluation

The framework was evaluated on two publicly available malware datasets, Mal‑Net and CICMalDroid2020. Experiments followed standard training‑validation splits, and performance was measured using accuracy, precision, recall, and F1‑score metrics.

Performance Compared to Baselines

Results reported in the abstract indicate that FOCA consistently outperformed unimodal baselines and surpassed most Euclidean multimodal approaches. The authors claim state‑of‑the‑art results relative to existing methods, with notable gains in F1‑score on both benchmark sets.

Implications for Malware Defense

If the reported improvements hold in broader deployments, the hyperbolic cross‑attention approach could offer security teams a more robust tool for early malware detection, especially in environments where binaries can be represented in multiple modalities. The paper suggests further exploration of hyperbolic embeddings for other cybersecurity tasks.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.