PerkinsFund/traceix-ai-security-telemetry

Traceix AI Security Telemetry

Each dataset is a JSONL file where each line describes a single file analyzed by Traceix. For every file you get:

• file_capabilities – high-level behaviors and capabilities (CAPA-style + mapped to ATT&CK and MBC tags like Execution/T1129, Discovery/T1083, etc.).
• file_exif_data – parsed EXIF metadata (file size, type, timestamps, company/product info, subsystem, linker/OS versions, etc.).
• model_classification_info – Traceix model verdict (safe / malicious), classification timestamp, and inference latency in seconds.
• decrypted_training_data – numeric feature vector actually used for training/inference (PE header fields, section statistics, imports/resources counts, entropy stats, etc.).
• metadata – model version and accuracy, upload metadata (timestamp, SHA-256, license), and payment information (THRT amount, Solana transaction hash + explorer URL, price at time of payment).

All records are focused on malware analysis and are stored in JSONL format. Datasets are automatically exported by Traceix on a monthly schedule and published as-is under the CC BY 4.0 license.

You can quickly load and sanity-check any monthly corpus using:

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))