Ensemble Boosting-Based Source Code Obfuscation Using Deep Learning Models for .NET
Year: 2026
Type: conference-paper
Abstract: Protecting. NET software against reverse engineering remains difficult because managed assemblies preserve rich structural information in Intermediate Language (IL) and metadata. This paper proposes an ensemble boosting-based obfuscation approach for. NET assemblies that combines three complementary deep learning components: an LSTM module for sequential IL dependencies, an autoencoder for latent structural transformation, and a transformer for global context modeling. Unlike static heuristic obfuscators, the proposed method selects transformation strategies adaptively at fragment level and aggregates model outputs through a boosting meta-stage. To ensure experimental reproducibility without relying on proprietary industrial software, a synthetic benchmark of managed-code fragments is introduced. The benchmark is generated from C# templates and evaluated at IL-token level using six obfuscation strategy classes: renaming, control-flow transformation, opaque predicate insertion, string encryption, dead-code insertion, and metadata-oriented protection. Experiments show that the boosting ensemble outperforms individual base models in transformation-selection accuracy. Compared with static baselines, the proposed method yields a more balanced trade-off among complexity growth, entropy increase, structural diversity, and overhead.
Authors: Igor Golovko, Oleg Savenko, Serhii Svystun
Institution: Khmelnytskyi National University
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7214656338