Update README.md

README.md

@@ -17,7 +17,7 @@ language:
 pretty_name: MH-100K Android Malware Dataset
 ---
 
-# MH-100K: A Comprehensive Android Malware Dataset
+# MH-100K: An innovative Android Malware Dataset for advanced research
 
 ## Dataset Summary
 
@@ -40,37 +40,42 @@ You can load this dataset directly using the Hugging Face `datasets` library.
 ### Quick Load
 
 ```python
-from datasets import load_dataset
+from huggingface_hub import hf_hub_download
 import pandas as pd
 
-# Load the main parquet file
-dataset = load_dataset("hendriow/mh100k", data_files="mh100.parquet", split="train")
+# 1. Download the specific file to your cache
+file_path = hf_hub_download(
+    repo_id="hendriow/mh100k",
+    filename="mh100.parquet",
+    repo_type="dataset"
+)
+
+# 2. Read it directly into a dataframe
+df = pd.read_parquet(file_path)
+
+df.info()
 
-# Example: Inspect the first instance
-print(dataset[0])
 ```
 
 ### Loading with Feature Names
 
-Since the dataset is high-dimensional (>24k features), the columns in the parquet file might be indexed. You can map them back to their real names (e.g., `android.permission.INTERNET`) using the `feature_names.csv` file.
+Since the dataset is high-dimensional (>10k features), the columns in the parquet file might be indexed. You can map them back to their real names (e.g., `android.permission.INTERNET`) using the `feature_names.csv` file.
 
 ```python
-from datasets import load_dataset
+from huggingface_hub import hf_hub_download
 import pandas as pd
 
-# 1. Load the Data
-dataset = load_dataset("hendriow/mh100k", data_files="mh100.parquet", split="train")
+# 1. Download the labels file to your local cache
+csv_path = hf_hub_download(
+    repo_id="hendriow/mh100k",
+    filename="mh100-labels.csv",
+    repo_type="dataset"
+)
 
-# 2. Load the Feature Names
-# We use pandas to read the feature mapping file directly from the repo URL or locally
-repo_url = "[https://huggingface.co/datasets/hendriow/mh100k/resolve/main/feature_names.csv](https://huggingface.co/datasets/hendriow/mh100k/resolve/main/feature_names.csv)"
-feature_map = pd.read_csv(repo_url)
-feature_names_list = feature_map['feature_name'].tolist()
+# 2. Read into a DataFrame
+labels_df = pd.read_csv(csv_path)
 
-# 3. (Optional) Convert to Pandas to see named columns
-df = dataset.select(range(100)).to_pandas()
-df.columns = feature_names_list + ['label'] # Assuming last col is label, adjust logic as needed
-print(df.head())
+labels_df.head()
 ```
 
 
@@ -104,17 +109,18 @@ If you use this dataset in your research, please cite the original authors:
 > @article{bragancca2023android,
   title={Android malware detection with MH-100K: An innovative dataset for advanced research},
   author={Bragan{\c{c}}a, Hendrio and Rocha, Vanderson and Barcellos, Lucas and Souto, Eduardo and Kreutz, Diego and Feitosa, Eduardo},
-  journal={Data in Brief},
-  volume={51},
-  pages={109750},
-  year={2023},
-  publisher={Elsevier}
+    journal={Data in Brief},
+    volume={51},
+    pages={109750},
+    year={2023},
+    publisher={Elsevier}
+}
+
+> @inproceedings{bragancca2023capturing,   
+	title={Capturing the behavior of android malware with mh-100k: A novel and multidimensional dataset},   
+	author={Bragan{\c{c}}a, Hendrio and Rocha, Vanderson and Barcellos, Lucas Vilanova and Souto, Eduardo and Kreutz, Diego and Feitosa, Eduardo},   
+	booktitle={Simp{\'o}sio Brasileiro de Seguran{\c{c}}a da Informa{\c{c}}{\~a}o e de Sistemas Computacionais  (SBSeg)},   
+	pages={510--515},   
+	year={2023},   
+	organization={SBC} 
 }
-> 
-> @inproceedings{bragancca2023capturing,   title={Capturing the behavior
-> of android malware with mh-100k: A novel and multidimensional
-> dataset},   author={Bragan{\c{c}}a, Hendrio and Rocha, Vanderson and
-> Barcellos, Lucas Vilanova and Souto, Eduardo and Kreutz, Diego and
-> Feitosa, Eduardo},   booktitle={Simp{\'o}sio Brasileiro de
-> Seguran{\c{c}}a da Informa{\c{c}}{\~a}o e de Sistemas Computacionais
-> (SBSeg)},   pages={510--515},   year={2023},   organization={SBC} }