BODMAS

The BODMAS dataset contains 134,435 samples and is used to evaluate the performance of machine learning classifiers and generative AI in cybersecurity threat detection.

Papers using BODMAS (9)

A hierarchical deep learning framework with doubly regularized loss for robust malware detection and family categorization2026 · 1 citesSEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget2026Beyond performance metrics: evaluating the unique value of generative AI in hybrid cybersecurity threat detection2026Machine Learning Transferability for Malware Detection2026CAFE-GB: Scalable and Stable Feature Selection for Malware Detection via Chunk-wise Aggregated Gradient Boosting2026ByteShield: Adversarially Robust End-to-End Malware Detection through Byte Masking2025MeLeMaD: Adaptive Malware Detection via Chunk-wise Feature Selection and Meta-Learning2025Efficient Malware Detection with Optimized Learning on High-Dimensional Features2025Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing2023 · 1 cites