Duplicate from ealvaradob/phishing-dataset

Co-authored-by: Esteban Alvarado <ealvaradob@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
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+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
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+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+test_all.json filter=lfs diff=lfs merge=lfs -text
+train_all.json filter=lfs diff=lfs merge=lfs -text
+test_dataset.json filter=lfs diff=lfs merge=lfs -text
+train_dataset.json filter=lfs diff=lfs merge=lfs -text
+test.json filter=lfs diff=lfs merge=lfs -text
+train.json filter=lfs diff=lfs merge=lfs -text
+combined_full.json filter=lfs diff=lfs merge=lfs -text
+combined_reduced.json filter=lfs diff=lfs merge=lfs -text
+texts.json filter=lfs diff=lfs merge=lfs -text
+urls.json filter=lfs diff=lfs merge=lfs -text
+webs.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+task_categories:
+- text-classification
+language:
+- en
+size_categories:
+- 10K<n<100K
+tags:
+- phishing
+- url
+- html
+- text
+---
+# Phishing Dataset
+
+Phishing datasets compiled from various resources for classification and phishing detection tasks.
+
+## Dataset Details
+
+All datasets have been preprocessed in terms of eliminating null, empty and duplicate data. Class balancing has also been performed to avoid possible biases. 
+Datasets have the same structure of two columns: `text` and `label`. Text field can contain samples of:
+
+- URL
+- SMS messages
+- Email messages
+- HTML code
+
+Depending on the dataset it belongs to; if it is the combined dataset it will have all data types. In addition, all records are labeled as **1 (Phishing)** or **0 (Benign)**.
+
+### Source Data
+
+Datasets correspond to a compilation of 4 sources, which are described below:
+
+- [Mail dataset](https://www.kaggle.com/datasets/subhajournal/phishingemails) that specifies the body text of various emails that can be used to detect phishing emails,
+  through extensive text analysis and classification with machine learning. Contains over 18,000 emails
+  generated by Enron Corporation employees.
+  
+- [SMS message dataset](https://data.mendeley.com/datasets/f45bkkt8pr/1) of more than 5,971 text messages. It includes 489 Spam messages, 638 Smishing messages
+  and 4,844 Ham messages. The dataset contains attributes extracted from malicious messages that can be used
+  to classify messages as malicious or legitimate. The data was collected by converting images obtained from
+  the Internet into text using Python code.
+
+- [URL dataset](https://www.kaggle.com/datasets/harisudhan411/phishing-and-legitimate-urls) with more than 800,000 URLs where 52% of the domains are legitimate and the remaining 47% are
+  phishing domains. It is a collection of data samples from various sources, the URLs were collected from the
+  JPCERT website, existing Kaggle datasets, Github repositories where the URLs are updated once a year and
+  some open source databases, including Excel files.
+
+- [Website dataset](https://data.mendeley.com/datasets/n96ncsr5g4/1) of 80,000 instances of legitimate websites (50,000) and phishing websites (30,000). Each
+  instance contains the URL and the HTML page. Legitimate data were collected from two sources: 1) A simple
+  keyword search on the Google search engine was used and the first 5 URLs of each search were collected.
+  Domain restrictions were used and a maximum of 10 collections from one domain was limited to have a diverse
+  collection at the end. 2) Almost 25,874 active URLs were collected from the Ebbu2017 Phishing Dataset
+  repository. Three sources were used for the phishing data: PhishTank, OpenPhish and PhishRepo.
+
+> It is worth mentioning that, in the case of the website dataset, it was unfeasible to bring the total 80,000 samples due to the heavy processing required.
+> It was limited to search the first 30,000 samples, of which only those with a weight of less than 100KB were used. This will make it easier to use the website dataset if you do not
+> have powerful resources.
+
+### Combined dataset
+
+The combined dataset is the one used to train BERT in phishing detection. But, in this repository you can notice that there are 
+two datasets named as **combined**:
+
+- combined full
+- combined reduced
+
+Combined datasets owe their name to the fact that they combine all the data sources mentioned in the previous section. However, there is a notable difference between them:
+
+- The full combined dataset contains the 800,000+ URLs of the URL dataset.
+- The reduced combined dataset reduces the URL samples by 95% in order to keep a more balanced combination of data.
+
+Why was that elimination made in the reduced combined dataset? Completely unifying all URL samples would make URLs 97% of the total, and emails, SMS and websites just 3%. 
+Missing data types from specific populations could bias the model and not reflect the realities of the environment in which it is run. There would be no representativeness 
+for the other data types and the model could ignore them. In fact, a test performed on the combined full dataset showed deplorable results in phishing classification with BERT. 
+Therefore it is recommended to use the reduced combined dataset. The combined full dataset was added for experimentation only.
+
+#### Processing combined reduced dataset
+
+Primarily, this dataset is intended to be used in conjunction with the BERT language model. Therefore, it has 
+not been subjected to traditional preprocessing that is usually done for NLP tasks, such as Text Classification. 
+
+_You may be wondering, is stemming, lemmatization, stop word removal, etc., necessary to improve the performance of BERT?_
+
+In general, **NO**. Preprocessing will not change the output predictions. In fact, removing empty words (which 
+are considered noise in conventional text representation, such as bag-of-words or tf-idf) can and probably will
+worsen the predictions of your BERT model. Since BERT uses the self-attenuation mechanism, these "stop words" 
+are valuable information for BERT. The same goes for punctuation: a question mark can certainly change the 
+overall meaning of a sentence. Therefore, eliminating stop words and punctuation marks would only mean 
+eliminating the context that BERT could have used to get better results.
+
+However, if this dataset plans to be used for another type of model, perhaps preprocessing for NLP tasks should 
+be considered. That is at the discretion of whoever wishes to employ this dataset. 
+
+For more information check these links:
+
+- https://stackoverflow.com/a/70700145
+- https://datascience.stackexchange.com/a/113366
+
+### How to use them
+
+You can easily use any of these datasets by specifying its name in the following code configuration:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("ealvaradob/phishing-dataset", "<desired_dataset>", trust_remote_code=True)
+```
+
+For example, if you want to load combined reduced dataset, you can use:
+
+```python
+dataset = load_dataset("ealvaradob/phishing-dataset", "combined_reduced", trust_remote_code=True)
+```
+
+Due to the implementation of the datasets library, when executing these codes you will see that only a training split is generated. 
+The entire downloaded dataset will be inside that split. But if you want to separate it into test and training sets, you could run this code:
+
+```python
+from datasets import Dataset
+from sklearn.model_selection import train_test_split
+
+df = dataset['train'].to_pandas()
+train, test = train_test_split(df, test_size=0.2, random_state=42)
+train, test = Dataset.from_pandas(train, preserve_index=False), Dataset.from_pandas(test, preserve_index=False)
+```

combined_full.json

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+size 590657583

combined_reduced.json

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+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
+TEXTS.csv filter=lfs diff=lfs merge=lfs -text
+URLS.csv filter=lfs diff=lfs merge=lfs -text
+WEBS.csv filter=lfs diff=lfs merge=lfs -text
+combined_all.json filter=lfs diff=lfs merge=lfs -text
+combined_reduced.json filter=lfs diff=lfs merge=lfs -text
+texts.json filter=lfs diff=lfs merge=lfs -text
+urls.json filter=lfs diff=lfs merge=lfs -text
+webs.json filter=lfs diff=lfs merge=lfs -text
+combined_full.json filter=lfs diff=lfs merge=lfs -text

phishing-dataset.py

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+# 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: Address all TODOs and remove all explanatory comments
+"""TODO: Add a description here."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{ealvaradob:dataset,
+title = {Phishing Datasets},
+author={Esteban Alvarado},
+year={2024}
+}
+"""
+
+_DESCRIPTION = """\
+Dataset designed for phishing classification tasks in various data types.
+"""
+_HOMEPAGE = ""
+
+_LICENSE = ""
+
+_URLS = {
+    "texts": "texts.json",
+    "urls": "urls.json",
+    "webs": "webs.json",
+    "combined_full": "combined_full.json",
+    "combined_reduced": "combined_reduced.json"
+}
+
+
+class PhishingDatasets(datasets.GeneratorBasedBuilder):
+    """Phishing Datasets Configuration"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="texts", version=VERSION, description="text subset"),
+        datasets.BuilderConfig(name="urls", version=VERSION, description="urls subset"),
+        datasets.BuilderConfig(name="webs", version=VERSION, description="webs subset"),
+        datasets.BuilderConfig(name="combined_full", version=VERSION, description="combined dataset that have all URLs"),
+        datasets.BuilderConfig(name="combined_reduced", version=VERSION, description="combined dataset that doesn't have all URLs for representativity issues"),
+    ]
+
+    DEFAULT_CONFIG_NAME = "combined_reduced"
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "text": datasets.Value("string"),
+                "label": datasets.Value("int64"),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=("text", "label"),
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS[self.config.name]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepath": data_dir,
+                    "split": "train",
+                },
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for index, sample in enumerate(data):
+                yield index, {
+                    "text": sample['text'],
+                    "label": sample['label']
+                }

texts.json

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urls.json

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webs.json

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