# Traceix AI Security Telemetry These files are uploaded monthly automatically by Traceix and provided as is under the CC BY 4.0 license. You can test the datasets simply by doing the following: ```python # do_dataset_publishing() from datasets import load_dataset import pandas as pd from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split # Load the Traceix telemetry dataset ds = load_dataset( "PerkinsFund/traceix-ai-security-telemetry", data_files="traceix-telemetry-corpus-2025-12.jsonl", # Or whatever month you want split="train", ) # We will need to flatten nested JSON into columns df = ds.to_pandas() df_flat = pd.json_normalize(df.to_dict(orient="records")) # Define the features and label based on schema feature_cols = [ "decrypted_training_data.SizeOfCode", "decrypted_training_data.SectionsMeanEntropy", "decrypted_training_data.ImportsNb", ] label_col = "model_classification_info.identified_class" # We don't have to but we will drop the rows with missing data df_flat = df_flat.dropna(subset=feature_cols + [label_col]) X = df_flat[feature_cols].values y = (df_flat[label_col] == "malicious").astype(int) # Start the training and test split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42 ) # Test the basic file classifier clf = LogisticRegression(max_iter=1000) clf.fit(X_train, y_train) print("Test accuracy:", clf.score(X_test, y_test)) ``` --- license: cc-by-4.0 tags: - malware - cybersecurity - ATT&CK - MBC - pe-files - elf-files - binary-classification - tabular-data - threat-intelligence - digital-forensics - reverse-engineering - incident-response - security-telemetry - ai-security - security-ml - mitre-attack - mitre-mbc - windows - linux - executable-files - static-analysis - behavioral-analysis - classification - anomaly-detection - intrusion-detection - explainable-ai - model-evaluation - benchmarking - training - evaluation - research - education - teaching pretty_name: traceix-corpus ---