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--- |
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license: cc-by-4.0 |
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tags: |
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- malware |
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- cybersecurity |
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- ATT&CK |
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- MBC |
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- pe-files |
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- elf-files |
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- binary-classification |
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- tabular-data |
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- threat-intelligence |
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- digital-forensics |
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- reverse-engineering |
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- incident-response |
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- security-telemetry |
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- ai-security |
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- security-ml |
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- mitre-attack |
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- mitre-mbc |
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- windows |
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- linux |
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- executable-files |
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- static-analysis |
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- behavioral-analysis |
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- classification |
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- anomaly-detection |
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- intrusion-detection |
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- explainable-ai |
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- model-evaluation |
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- benchmarking |
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- training |
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- evaluation |
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- research |
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- education |
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- teaching |
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pretty_name: traceix-ai-telemetry |
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--- |
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# Traceix AI Security Telemetry |
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Each dataset is a JSONL file where **each line describes a single file analyzed by [Traceix](https://traceix.com)**. For every file you get: |
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* **`file_capabilities`** – high-level behaviors and capabilities (CAPA-style + mapped to ATT&CK and MBC tags like `Execution/T1129`, `Discovery/T1083`, etc.). |
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* **`file_exif_data`** – parsed EXIF metadata (file size, type, timestamps, company/product info, subsystem, linker/OS versions, etc.). |
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* **`model_classification_info`** – [Traceix](https://traceix.com) model verdict (`safe` / `malicious`), classification timestamp, and inference latency in seconds. |
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* **`decrypted_training_data`** – numeric feature vector actually used for training/inference (PE header fields, section statistics, imports/resources counts, entropy stats, etc.). |
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* **`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). |
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All records are focused on **malware analysis** and are stored in **JSONL format**. |
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Datasets are **automatically exported by [Traceix](https://traceix.com) on a monthly schedule** and published **as-is** under the **CC BY 4.0** license. |
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You can quickly load and sanity-check any monthly corpus using: |
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```python |
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from datasets import load_dataset |
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import pandas as pd |
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from sklearn.linear_model import LogisticRegression |
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from sklearn.model_selection import train_test_split |
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# Load the Traceix telemetry dataset |
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ds = load_dataset( |
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"PerkinsFund/traceix-ai-security-telemetry", |
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data_files="traceix-telemetry-corpus-2025-12.jsonl", # Or whatever month you want |
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split="train", |
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) |
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# We will need to flatten nested JSON into columns |
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df = ds.to_pandas() |
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df_flat = pd.json_normalize(df.to_dict(orient="records")) |
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# Define the features and label based on schema |
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feature_cols = [ |
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"decrypted_training_data.SizeOfCode", |
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"decrypted_training_data.SectionsMeanEntropy", |
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"decrypted_training_data.ImportsNb", |
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] |
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label_col = "model_classification_info.identified_class" |
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# We don't have to but we will drop the rows with missing data |
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df_flat = df_flat.dropna(subset=feature_cols + [label_col]) |
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X = df_flat[feature_cols].values |
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y = (df_flat[label_col] == "malicious").astype(int) |
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# Start the training and test split |
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X_train, X_test, y_train, y_test = train_test_split( |
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X, y, test_size=0.2, random_state=42 |
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) |
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# Test the basic file classifier |
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clf = LogisticRegression(max_iter=1000) |
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clf.fit(X_train, y_train) |
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print("Test accuracy:", clf.score(X_test, y_test)) |
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``` |
