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Found 6 papers, 3 papers with code
Adversarial Attacks against Binary Similarity Systems
In recent years, binary analysis gained traction as a fundamental approach to inspect software and guarantee its security.
BinBert: Binary Code Understanding with a Fine-tunable and Execution-aware Transformer
An instruction embedding model is a neural network that transforms sequences of assembly instructions into embedding vectors.
In Nomine Function: Naming Functions in Stripped Binaries with Neural Networks
We investigate the applicability of tine-tuning (i. e., taking a model already trained on a large generic corpora and retraining it for a specific task).
SAFE: Self-Attentive Function Embeddings for Binary Similarity
We report the results from a quantitative and qualitative analysis that show how SAFE provides a noticeable performance improvement with respect to previous solutions.
Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks
Furthermore, we report on a qualitative analysis of functions embeddings.
Android Malware Family Classification Based on Resource Consumption over Time
An important task of malware analysis is the classification of malware samples into known families.
Cryptography and Security
