These are all scripts for reproducibility of "Generating Robust Representations of Structures in OpenSSH Heap Dumps". save_and_reduce_struct_dataset.py - Extract raw data structures from heap dumps dataset and label. masking_pretraining.py - Pre-Training with Masked Language Modeling of DistilBERT Transformer seq-class_finetuning.py - Fine-Tuning of that Transformer with Sequence Classification extract_embeddings_dataloader.py - Run inference on Transformer model and extract embeddings in the process. pooling_network.py - Run embeddings through a neural network with a configurable hidden size. Can be used to extract compressed embeddings. SVMClassifier.py - Classify embeddings. classes.py - Helper for data structure dataset creation. Associated Datasets: structs_eval.zip - Raw data structures extracted from heap dump and labelled (Validation). Also available: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_eval structs_train.zip - Raw data structures extracted from heap dump and labelled (Training). Also available: https://huggingface.co/datasets/johannes-garstenauer/structs_token_size_4_reduced_labelled_train embeddings_from_distilbert_masking_heaps.zip - Embeddings extracted from pre-trained transformer. Also available: https://huggingface.co/datasets/johannes-garstenauer/embeddings_from_distilbert_masking_heaps embeddings_from_distilbert_class_heaps.zip - Embeddings extracted from fine-tuned transformer. Also available: https://huggingface.co/datasets/johannes-garstenauer/embeddings_from_distilbert_class_heaps Heap Dumps available at: https://zenodo.org/records/6537904 These are the Transformer models generated in the process: distilbert_masking_heaps.zip - Pre-trained Transformer. Load using HuggingFace library. Also available: https://huggingface.co/johannes-garstenauer/distilbert_masking_heaps distilbert_class_heaps.zip - FIne-tuned Transformer. Load using HuggingFace library. Also available: https://huggingface.co/johannes-garstenauer/distilbert_class_heaps