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README.md

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+---
+# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
+license: cc-by-4.0
+tags:
+- security
+- malware-detection
+- android
+- graph-machine-learning
+- distribution-shift
+- function-call-graphs
+---
+
+# Dataset Card for MalNet-Tiny Distribution Shift Benchmarks
+
+This dataset contains three datasets, **MalNet-Tiny**, **MalNet-Tiny-Common** and **MalNet-Tiny-Distinct**, designed to evaluate the
+robustness of graph-based Android malware classifiers under distribution shift. Derived from the [MalNet-Tiny](https://malnet.cs.gatech.edu/)
+dataset, these benchmarks introduce specific partitions to simulate realistic domain (cross-family) and temporal shifts by enriching Function
+Call Graphs (FCGs) with semantic function metadata and LLM-based code embeddings.
+
+## Dataset Details
+
+### Dataset Description
+
+The dataset consists of Android Function Call Graphs (FCGs) where nodes represent functions and edges represent invocations. Unlike the original MalNet-Tiny, which relies on structure-only representations, this dataset enriches the graphs with:
+1.  **Function Metadata:** Lightweight features such as function names, method signatures, and access flags.
+2.  **LLM Embeddings:** Dense semantic representations of function bodies derived from Large Language Models (LLMs), extracted when source code is available.
+
+The available datasets are defined as follows:
+* **MalNet-Tiny:** The original MalNet-Tiny dataset with semantic features for nodes in the FCGs.
+* **MalNet-Tiny-Common:** Evaluates generalization of the model under covariate shift.
+* **MalNet-Tiny-Distinct:** valuates generalization of the model under domain shift.
+
+### Dataset Specification
+- **Language(s) (NLP):** Python
+- **License:** Creative Commons Attribution 4.0 International (CC-BY 4.0)
+
+## Uses
+
+### Direct Use
+
+* **Robust Malware Detection:** Developing and benchmarking Graph Neural Networks (GNNs) that are resilient to evolving malware variants.
+* **Distribution Shift Evaluation:** Testing model performance under covariate shift (Common) and domain shift (Distinct).
+* **Graph Representation Learning:** Studying the integration of structural (graph) and semantic (LLM/Metadata) features in learning tasks.
+
+### Out-of-Scope Use
+
+* This dataset is intended for research purposes (defense) and should not be used to generate or obfuscate malware.
+* The semantic features rely on static analysis; dynamic execution traces are not included.
+
+## Dataset Structure
+
+The dataset is composed of `.pt` files to be loaded into PyTorch Geometric. Accompanied code will be provided after publication.
+
+## Dataset Creation
+
+### Curation Rationale
+
+Existing graph-based classifiers achieve high accuracy on standard benchmarks (like MalNet-Tiny) but suffer performance drops of up to 45% on unseen families. These benchmarks were created to rigorously evaluate and improve the generalization capabilities of malware detectors in realistic, evolving threat environments.
+
+### Source Data
+
+The data are processed from raw APK files from [AndroZoo](https://androzoo.uni.lu/), a repository of real-world Android packages.
+Labels are derived from [MalNet](https://malnet.cs.gatech.edu/), a large-scale dataset containing Android Function Call Graphs and their malware classifications.
+
+#### Data Collection and Processing
+
+1.  **Base Data:** Samples and labels were selected from MalNet, then download the corresponding raw APK from AndroZoo.
+    * *MalNet-Tiny:* The split from the original MalNet.
+    * *MalNet-Tiny-Common:* Samples from **overlapping** malware *families* but different malware *types*.
+    * *MalNet-Tiny-Distinct:* Samples from **completely unseen** families of malwares.
+2.  **Feature Extraction:**
+    * *Metadata Extraction:* Function names, signatures, and flags were extracted to provide lightweight semantic context.
+    * *LLM Embedding:* Source code (decompiled Smali/Java) of function bodies was processed using Large Language Models to generate dense code embeddings.
+
+## Bias, Risks, and Limitations
+
+* **Static Analysis Limitations:** The graphs are based on static analysis and may be vulnerable to obfuscation techniques that alter call graphs (e.g., reflection, dynamic loading) without changing behavior.
+* **Feature Availability:** LLM embeddings depend on the successful decompilation and availability of function bodies.
+
+### Recommendations
+
+Users should be made aware of the risks, biases, and limitations of the dataset. Models trained on this dataset should be evaluated in conjunction with dynamic analysis methods for deployment in critical security environments.
+
+## Citation
+Will be added later.