Malware Type Detection Dataset

A dataset for malware classification tasks, sourced from the UCI Machine Learning Repository. It is intended for building and evaluating models that can identify different types of malicious software. The specific temporal coverage and collection method are not detailed.

Use Cases

• Classify malware types using features like file size, API call sequences, or network behavior patterns.
• Train a binary classifier to distinguish benign software from malicious samples based on extracted static or dynamic analysis features.
• Benchmark feature selection methods for identifying the most predictive attributes for malware family identification.
• Analyze the distribution of malware types or families present in the dataset to understand threat landscapes.

Strengths

• Sourced from the authoritative UCI Machine Learning Repository, ensuring a standard format and baseline for comparison.
• Designed for a specific, well-defined machine learning task (malware type detection).

Limitations

• The exact number of samples, features, and class balance is unknown, making it difficult to assess model scalability.
• The dataset's age and the specific malware families represented may not reflect current threat environments.
• Lack of detailed column descriptions limits understanding of the feature engineering and data provenance.

Provenance

Source

UCI Machine Learning Repository

Collection Method

null

Time Range

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Freshness

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Geography

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