Datasets:

darkknight25
/

Advanced_SIEM_Dataset

@@ -159,7 +159,7 @@ X = scaler.fit_transform(df[['risk_score', 'confidence']])
 # Categorical Features:
-```
 Encode event_type, severity, and other categorical fields using LabelEncoder or one-hot encoding:from sklearn.preprocessing import LabelEncoder
 le = LabelEncoder()
@@ -198,7 +198,7 @@ Convert timestamp to datetime for temporal analysis:df['timestamp'] = pd.to_date
 Below are example ML/AI workflows using Hugging Face and other libraries.
 Anomaly Detection (Isolation Forest)
 Detect unusual events like zero-day exploits or beaconing:
-```pyhton
 from datasets import load_dataset
 from sklearn.ensemble import IsolationForest
 import pandas as pd
@@ -214,7 +214,7 @@ print(f"Detected {len(anomalies)} anomalies")
 ```
 Threat Classification (Transformers)
 Classify events by severity using a BERT model:
-```pyhton
 from datasets import load_dataset
 from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
 from sklearn.preprocessing import LabelEncoder
@@ -246,7 +246,7 @@ trainer.train()
 ```
 Time-Series Forecasting (Prophet)
 Forecast risk_score trends:
-```
 from datasets import load_dataset
 from prophet import Prophet
 import pandas as pd
@@ -281,19 +281,20 @@ auth_df['cluster'] = kmeans.fit_predict(X)
 print(auth_df.groupby('cluster')[['user', 'action']].describe())
 ```
 # Limitations
 Synthetic Nature: The dataset is synthetic and may not fully capture real-world SIEM log complexities, such as vendor-specific formats or noise patterns.
 Class Imbalance: Certain event_type (e.g., ai, iot) or severity (e.g., emergency) values may be underrepresented. Use data augmentation or reweighting for balanced training.
 Missing Values: Some dst_ip fields in ids_alert events are "N/A", requiring imputation or filtering.
 Timestamp Anomalies: 5% of records include intentional timestamp anomalies (future/past dates) to simulate time-based attacks, which may require special handling.
-Bias and Ethical Considerations
 The dataset includes synthetic geo_location data with a 5% chance of high-risk locations (e.g., North Korea, Russia). This is for anomaly simulation and not indicative of real-world biases. Ensure models do not inadvertently profile based on geo_location.
 User names and other PII-like fields are generated using faker and do not represent real individuals.
 Models trained on this dataset should be validated to avoid overfitting to synthetic patterns.
 # Citation
 If you use this dataset in your work, please cite:
 @dataset{advanced_siem_dataset_2025,
   author = {sunnythakur},
@@ -302,10 +303,11 @@ If you use this dataset in your work, please cite:
   publisher = {Hugging Face},
   url = {https://huggingface.co/datasets/darkknight25/advanced_siem_dataset}
 }
 # Acknowledgments
 Generated using a custom Python script (datasetcreator.py) with faker and numpy.
 Inspired by real-world SIEM log formats (e.g., CEF) and MITRE ATT&CK framework.
 Thanks to the Hugging Face community for providing tools to share and process datasets.

 # Categorical Features:
+```java
 Encode event_type, severity, and other categorical fields using LabelEncoder or one-hot encoding:from sklearn.preprocessing import LabelEncoder
 le = LabelEncoder()
 Below are example ML/AI workflows using Hugging Face and other libraries.
 Anomaly Detection (Isolation Forest)
 Detect unusual events like zero-day exploits or beaconing:
+```python
 from datasets import load_dataset
 from sklearn.ensemble import IsolationForest
 import pandas as pd
 ```
 Threat Classification (Transformers)
 Classify events by severity using a BERT model:
+```python
 from datasets import load_dataset
 from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments
 from sklearn.preprocessing import LabelEncoder
 ```
 Time-Series Forecasting (Prophet)
 Forecast risk_score trends:
+```java
 from datasets import load_dataset
 from prophet import Prophet
 import pandas as pd
 print(auth_df.groupby('cluster')[['user', 'action']].describe())
 ```
 # Limitations
+```java
 Synthetic Nature: The dataset is synthetic and may not fully capture real-world SIEM log complexities, such as vendor-specific formats or noise patterns.
 Class Imbalance: Certain event_type (e.g., ai, iot) or severity (e.g., emergency) values may be underrepresented. Use data augmentation or reweighting for balanced training.
 Missing Values: Some dst_ip fields in ids_alert events are "N/A", requiring imputation or filtering.
 Timestamp Anomalies: 5% of records include intentional timestamp anomalies (future/past dates) to simulate time-based attacks, which may require special handling.
+```
+# Bias and Ethical Considerations
+```
 The dataset includes synthetic geo_location data with a 5% chance of high-risk locations (e.g., North Korea, Russia). This is for anomaly simulation and not indicative of real-world biases. Ensure models do not inadvertently profile based on geo_location.
 User names and other PII-like fields are generated using faker and do not represent real individuals.
 Models trained on this dataset should be validated to avoid overfitting to synthetic patterns.
+```
 # Citation
+```
 If you use this dataset in your work, please cite:
 @dataset{advanced_siem_dataset_2025,
   author = {sunnythakur},
   publisher = {Hugging Face},
   url = {https://huggingface.co/datasets/darkknight25/advanced_siem_dataset}
 }
+```
 # Acknowledgments
+```
 Generated using a custom Python script (datasetcreator.py) with faker and numpy.
 Inspired by real-world SIEM log formats (e.g., CEF) and MITRE ATT&CK framework.
 Thanks to the Hugging Face community for providing tools to share and process datasets.
+```