BinAdapter: Leveraging Continual Learning for Inferring Function Symbol Names in a Binary
Nozima Murodova, Hyungjoon Koo · 2024
Binary reverse engineering is crucial to gaining insights into the inner workings of a stripped binary. Yet, it is challenging to read the original semantics from a binary code snippet because of the unavailability of high-level information in the source, such as function names, variable names, and types. Recent advancements in deep learning show the possibility of recovering such vanished information with a well-trained model from a pre-defined dataset. Albeit a static model's notable performance, it can hardly cope with an ever-increasing data stream (e.g., compiled binaries) by nature. The two viable approaches for ceaseless learning are retraining the whole dataset from scratch and fine-tuning a pre-trained model; however, retraining suffers from large computational overheads and fine-tuning from performance degradation (i.e., catastrophic forgetting). Lately, continual learning (CL) tackles the problem of handling incremental data in security domains (e.g., network intrusion detection, malware detection) using reasonable resources while maintaining performance in practice.