SMS PHISHING DATASET FOR MACHINE LEARNING AND PATTERN RECOGNITION

The dataset is a set of labelled text messages that have been collected for SMS Phishing research. It has 5971 text messages labeled as Legitimate (Ham) or Spam or Smishing. It includes 489 spam messages, 638 smishing messages, and 4844 ham messages. This dataset contains raw message content that can be used as labelled data in Deep Learning or for extracting further attributes. The dataset contains extracted attributes from malicious messages that can be used for Classification of messages as malicious or legitimate. This dataset also includes python code that are used for extracting attributes. The data has been collected by converting the images obtained from the Internet to text using Python code. Attributes have been selected based on their relevance. The details of dataset attributes are given below:

LABEL- Classification label categorizing the message as ham, spam, or Smishing
TEXT- The raw content of the message.
URL- Gives out whether the message contains a URL or not.
EMAIL- Gives out whether the message contains an email id or not.
PHONE - Gives out whether the message contains a phone number or not.
Python code for extraction of the above listed dataset attributes is attached. The snapshot of this dataset is also attached. Frequency chart of the attributes are also attached.

该数据集为用于短信钓鱼研究的标记文本消息集合。包含被标记为合法（Ham）、垃圾邮件或钓鱼的5971条文本消息。其中包含489条垃圾邮件、638条钓鱼邮件和4844条合法邮件。该数据集包含了可用于深度学习或进一步属性提取的原始消息内容。数据集中还包含了用于对消息进行恶意或合法分类的恶意消息提取属性。此外，该数据集还包括了用于提取上述属性的Python代码。数据通过将网络获取的图像转换为文本的方式收集。属性的选择基于其相关性。以下是数据集属性的详细信息：

-LABEL- 对消息进行合法、垃圾邮件或钓鱼分类的标签。
-TEXT- 消息的原始内容。
-URL- 标识消息是否包含URL。
-EMAIL- 标识消息是否包含电子邮件地址。
-PHONE- 标识消息是否包含电话号码。

附带的Python代码用于提取上述列出的数据集属性。同时，还附带了该数据集的快照以及属性频率图。