Reliable Detection of Compressed and Encrypted Data

TL;DR

This paper introduces EnCoD, a learning-based classifier that reliably distinguishes between compressed and encrypted data fragments across various file types and sizes, outperforming existing statistical methods.

Contribution

The paper presents EnCoD, a novel machine learning approach that improves detection accuracy and identifies data formats, addressing limitations of current statistical techniques.

Findings

EnCoD achieves 82-92% accuracy across fragment sizes.

Current statistical methods fail on compressed data.

EnCoD accurately identifies data formats.

Abstract

Several cybersecurity domains, such as ransomware detection, forensics and data analysis, require methods to reliably identify encrypted data fragments. Typically, current approaches employ statistics derived from byte-level distribution, such as entropy estimation, to identify encrypted fragments. However, modern content types use compression techniques which alter data distribution pushing it closer to the uniform distribution. The result is that current approaches exhibit unreliable encryption detection performance when compressed data appears in the dataset. Furthermore, proposed approaches are typically evaluated over few data types and fragment sizes, making it hard to assess their practical applicability. This paper compares existing statistical tests on a large, standardized dataset and shows that current approaches consistently fail to distinguish encrypted and compressed data on both small and large fragment sizes. We address these shortcomings and design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data. We evaluate EnCoD on a dataset of 16 different file types and fragment sizes ranging from 512B to 8KB. Our results highlight that EnCoD outperforms current approaches by a wide margin, with accuracy ranging from ~82 for 512B fragments up to ~92 for 8KB data fragments. Moreover, EnCoD can pinpoint the exact format of a given data fragment, rather than performing only binary classification like previous approaches.