Update README.md

README.md

@@ -15,14 +15,15 @@ tags:
 
 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, these benchmarks introduce specific partitions to simulate realistic covariate shift (intra-family) and domain (cross-family) 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:
+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.
 
@@ -50,7 +51,8 @@ The available datasets are defined as follows:
 
 ## Dataset Structure
 
-The dataset is composed of `.pt` files to be loaded into PyTorch Geometric. Accompanied code can be found at [this GitHub repository](https://github.com/ngoctnq/malnet-features).
+The dataset is composed of `.pt` files to be loaded into PyTorch Geometric. Accompanied code can be found at
+[this GitHub repository](https://github.com/ngoctnq/malnet-features).
 
 ## Dataset Creation
 
@@ -69,7 +71,7 @@ Labels are derived from [MalNet](https://malnet.cs.gatech.edu/), a large-scale d
 
 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-Common:* Samples from **same** 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.
@@ -77,19 +79,21 @@ Labels are derived from [MalNet](https://malnet.cs.gatech.edu/), a large-scale d
 
 ## 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.
+* **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.
+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
 ```latex
-@misc{tran2025mitigatingdistributionshiftgraphbased,
-      title={Mitigating Distribution Shift in Graph-Based Android Malware Classification via Function Metadata and LLM Embeddings}, 
+@misc{tran2026quantifyinggeneralizationgapnew,
+      title={Quantifying the Generalization Gap: A New Benchmark for Out-of-Distribution Graph-Based Android Malware Classification}, 
       author={Ngoc N. Tran and Anwar Said and Waseem Abbas and Tyler Derr and Xenofon D. Koutsoukos},
-      year={2025},
+      year={2026},
       eprint={2508.06734},
       archivePrefix={arXiv},
       primaryClass={cs.CR},