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README.md

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- expert-generated
-- found
-language:
-- code
-- en
-license:
-- gpl-3.0
-multilinguality:
-- translation
-size_categories:
-- unknown
-source_datasets:
-- original
-task_categories:
-- text-generation
-task_ids:
-- language-modeling
-paperswithcode_id: shellcode-ia32
----
-
-# Shellcode_IA32
-
-___Shellcode_IA32___ is a dataset containing _20_ years of shellcodes from a variety of sources is the largest collection of shellcodes in assembly available to date.
-
-This dataset consists of 3,200 examples of instructions in assembly language for _IA-32_ (the 32-bit version of the x86 Intel Architecture) from publicly available security exploits. We collected assembly programs used to generate shellcode from [exploit-db](https://www.exploit-db.com/shellcodes?platform=linux_x86) and from [shell-storm](http://shell-storm.org/shellcode/).
-We enriched the dataset by adding examples of assembly programs for the _IA-32_ architecture from popular tutorials and books. This allowed us to understand how different authors and assembly experts comment and, thus, how to deal with the ambiguity of natural language in this specific context. Our dataset consists of 10% of instructions collected from books and guidelines, and the rest from real shellcodes. 
-
-Our focus is on Linux, the most common OS for security-critical network services. Accordingly, we added assembly instructions written with _Netwide Assembler_ (NASM) for Linux.
-
-Each line of _Shellcode\_IA32_ dataset represents a snippet - intent pair. The _snippet_ is a line or a combination of multiple lines of assembly code, built by following the NASM syntax. The _intent_ is a comment in the English language.
-
-Further statistics on the dataset and a set of preliminary experiments performed with a neural machine translation (NMT) model are described in the following paper: [Shellcode_IA32: A Dataset for Automatic Shellcode Generation](https://arxiv.org/abs/2104.13100).
-
-
-**Note**: This work was done in collaboration with the [DESSERT Lab](http://www.dessert.unina.it/).
-
-The dataset is also hosted on the [DESSERT Lab Github](https://github.com/dessertlab/Shellcode_IA32).
-
-
-Please consider citing our work: 
-```
-@inproceedings{liguori-etal-2021-shellcode,
-    title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
-    author = "Liguori, Pietro  and
-      Al-Hossami, Erfan  and
-      Cotroneo, Domenico  and
-      Natella, Roberto  and
-      Cukic, Bojan  and
-      Shaikh, Samira",
-    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
-    month = aug,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.nlp4prog-1.7",
-    doi = "10.18653/v1/2021.nlp4prog-1.7",
-    pages = "58--64",
-    abstract = "We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.",
-}
-```

dataset_infos.json

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-{"default": {"description": "Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits.\n", "citation": "    @inproceedings{liguori-etal-2021-shellcode,\n    title = \"{S}hellcode{\\_}{IA}32: A Dataset for Automatic Shellcode Generation\",\n    author = \"Liguori, Pietro  and\n      Al-Hossami, Erfan  and\n      Cotroneo, Domenico  and\n      Natella, Roberto  and\n      Cukic, Bojan  and\n      Shaikh, Samira\",\n    booktitle = \"Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)\",\n    month = aug,\n    year = \"2021\",\n    address = \"Online\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2021.nlp4prog-1.7\",\n    doi = \"10.18653/v1/2021.nlp4prog-1.7\",\n    pages = \"58--64\",\n    abstract = \"We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.\",\n}\n", "homepage": "https://github.com/dessertlab/Shellcode_IA32", "license": "GNU GENERAL PUBLIC LICENSE", "features": {"intent": {"dtype": "string", "id": null, "_type": "Value"}, "snippet": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "shellcode_i_a32", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 185932, "num_examples": 2560, "dataset_name": "shellcode_i_a32"}, "test": {"name": "test", "num_bytes": 22540, "num_examples": 320, "dataset_name": "shellcode_i_a32"}, "validation": {"name": "validation", "num_bytes": 22806, "num_examples": 320, "dataset_name": "shellcode_i_a32"}}, "download_checksums": {"https://raw.githubusercontent.com/dessertlab/Shellcode_IA32/main/Shellcode_IA32.tsv": {"num_bytes": 212071, "checksum": "f2da8827140fafc9bdefa00cfad7ea79a1d783165bf47439bf36f93a9ae38df5"}}, "download_size": 212071, "post_processing_size": null, "dataset_size": 231278, "size_in_bytes": 443349}}

shellcode_i_a32.py

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-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""TODO: Add a description here."""
-
-
-import csv
-import json
-import os
-import pandas as pd
-import datasets
-
-
-# TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-    @inproceedings{liguori-etal-2021-shellcode,
-    title = "{S}hellcode{\_}{IA}32: A Dataset for Automatic Shellcode Generation",
-    author = "Liguori, Pietro  and
-      Al-Hossami, Erfan  and
-      Cotroneo, Domenico  and
-      Natella, Roberto  and
-      Cukic, Bojan  and
-      Shaikh, Samira",
-    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing for Programming (NLP4Prog 2021)",
-    month = aug,
-    year = "2021",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://aclanthology.org/2021.nlp4prog-1.7",
-    doi = "10.18653/v1/2021.nlp4prog-1.7",
-    pages = "58--64",
-    abstract = "We take the first step to address the task of automatically generating shellcodes, i.e., small pieces of code used as a payload in the exploitation of a software vulnerability, starting from natural language comments. We assemble and release a novel dataset (Shellcode{\_}IA32), consisting of challenging but common assembly instructions with their natural language descriptions. We experiment with standard methods in neural machine translation (NMT) to establish baseline performance levels on this task.",
-}
-"""
-
-# TODO: Add description of the dataset here
-# You can copy an official description
-_DESCRIPTION = """\
-Shellcode_IA32 is a dataset for shellcode generation from English intents. The shellcodes are compilable on Intel Architecture 32-bits.
-"""
-
-# TODO: Add a link to an official homepage for the dataset here
-_HOMEPAGE = "https://github.com/dessertlab/Shellcode_IA32"
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = "GNU GENERAL PUBLIC LICENSE"
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLs = {
-    'default': "https://raw.githubusercontent.com/dessertlab/Shellcode_IA32/main/Shellcode_IA32.tsv",
-}
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class ShellcodeIA32(datasets.GeneratorBasedBuilder):
-    """Shellcode_IA32 a dataset for shellcode generation"""
-
-    VERSION = datasets.Version("1.1.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    # BUILDER_CONFIGS = [
-    #     datasets.BuilderConfig(name="default", version=VERSION, description="This part of my dataset covers the default train/test split"),
-    #     #datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
-    # ]
-
-    DEFAULT_CONFIG_NAME = "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-
-            features = datasets.Features(
-                {
-                    "intent": datasets.Value("string"),
-                    "snippet": datasets.Value("string"),
-
-                }
-            )
-            return datasets.DatasetInfo(
-                # This is the description that will appear on the datasets page.
-                description=_DESCRIPTION,
-                # This defines the different columns of the dataset and their types
-                features=features,  # Here we define them above because they are different between the two configurations
-                # If there's a common (input, target) tuple from the features,
-                # specify them here. They'll be used if as_supervised=True in
-                # builder.as_dataset.
-                supervised_keys=None,
-                # Homepage of the dataset for documentation
-                homepage=_HOMEPAGE,
-                # License for the dataset if available
-                license=_LICENSE,
-                # Citation for the dataset
-                citation=_CITATION,
-            )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        my_urls = _URLs[self.config.name]
-        data_dir = dl_manager.download_and_extract(my_urls)
-        # return [
-        #     datasets.SplitGenerator(
-        #         name=datasets.Split.TRAIN,
-        #         # These kwargs will be passed to _generate_examples
-        #         gen_kwargs={
-        #             "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
-        #             "split": "train",
-        #         },
-        #     ),
-        #     datasets.SplitGenerator(
-        #         name=datasets.Split.TEST,
-        #         # These kwargs will be passed to _generate_examples
-        #         gen_kwargs={
-        #             "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
-        #             "split": "test"
-        #         },
-        #     ),
-        #     datasets.SplitGenerator(
-        #         name=datasets.Split.VALIDATION,
-        #         # These kwargs will be passed to _generate_examples
-        #         gen_kwargs={
-        #             "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
-        #             "split": "dev",
-        #         },
-        #     ),
-        # ]
-
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir),
-                    "split": "test"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir),
-                    "split": "dev",
-                },
-            ),
-        ]
-
-    def _generate_examples(
-        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    ):
-        """ Yields examples as (key, example) tuples. """
-        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is here for legacy reason (tfds) and is not important in itself.
-        """This function returns the examples in the raw (text) form."""
-
-        df = pd.read_csv(filepath, delimiter = '\t')
-        train = df.sample(frac = 0.8, random_state = 0)
-        test = df.drop(train.index)
-        dev = test.sample(frac = 0.5, random_state = 0)
-        test = test.drop(dev.index)
-
-        if split == 'train':
-            data = train
-        elif split == 'dev':
-            data = dev
-        elif split == 'test':
-            data = test
-
-        for idx, row in data.iterrows():
-            yield idx, {
-                "snippet": row["SNIPPETS"],
-                "intent": row["INTENTS"],
-
-            }
-            # with open(filepath, encoding="utf-8") as f:
-            #     reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
-            #     reader =
-            #     for idx, row in enumerate(reader):
-            #
-            #         yield idx, {
-            #             "snippet": row["SNIPPETS"],
-            #             "intent": row["INTENTS"],
-            #
-            #         }