Upload new phishing datasets

.gitattributes

@@ -59,3 +59,8 @@ 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

@@ -14,22 +14,23 @@ tags:
 ---
 # Phishing Dataset
 
-Phishing dataset compiled from various resources for classification and phishing detection tasks.
+Phishing datasets compiled from various resources for classification and phishing detection tasks.
 
 ## Dataset Details
 
-The dataset has two columns: `text` and `label`. Text field contains samples of:
+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
 
-Which are labeled as **1 (Phishing)** or **0(Benign)**.
+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
 
-This dataset is a compilation of 4 sources, which are described below:
+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
@@ -52,12 +53,34 @@ This dataset is a compilation of 4 sources, which are described below:
   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.
 
-#### Dataset Processing
+> 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. 
 
-_Is stemming, lemmatization, stop word removal, etc., necessary to improve the performance of BERT?_
+_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
@@ -72,4 +95,32 @@ be considered. That is at the discretion of whoever wishes to employ this datase
 For more information check these links:
 
 - https://stackoverflow.com/a/70700145
-- https://datascience.stackexchange.com/a/113366
+- 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-datasets", "<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-datasets", "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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+oid sha256:222fd5f841a0565841f8ede2e21b9b48cfea933a6b5c3a7da3e3a2cbc156d3d5
+size 521149713

gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.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
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.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
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.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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+size 52079789

urls.json

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+size 73157496

webs.json

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