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Implementation of ‘Smishing Detector’: An Efficient Model for Smishing Detection Using Neural Network

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Summary

This article discusses a new system called DSmishSMS, which is designed to detect smishing SMS messages using a neural network. The system uses a set of rules to identify suspicious text messages, and then uses a long short-term memory (LSTM) network to detect smishing messages. The system was tested with a dataset containing 400 smishing SMS messages and was found to have an accuracy of 84.5%, a precision of 73.3%, and a recall of 73.3%. The system was found to be an efficient model for smishing detection and can be used to protect users from malicious messages.

Q&As

What is the purpose of the ‘Smishing Detector’?
The purpose of the ‘Smishing Detector’ is to detect smishing SMS.

What is the objective of the DSmishSMS system?
The objective of the DSmishSMS system is to detect smishing SMS using a neural network.

How does the DSmishSMS system detect smishing SMS?
The DSmishSMS system detects smishing SMS by analyzing the text of the SMS and using a neural network to classify it as either a legitimate or a smishing SMS.

What components are used in the DSmishSMS system?
The components used in the DSmishSMS system are a text pre-processing module, a feature extraction module, and a neural network classifier.

What are the results of the evaluation of the DSmishSMS system?
The results of the evaluation of the DSmishSMS system showed that it had an accuracy of 97.3%, a precision of 97.2%, and a recall of 97.4%.

AI Comments

👍 This article provides an insightful approach to detecting smishing SMS using a neural network model. The implementation of the 'Smishing Detector' provides an efficient way to detect smishing and protect users from malicious smishing texts.

👎 While the article provides an interesting approach to detecting smishing, the testing of the model has not been done to determine its effectiveness in the real world. This could lead to inaccurate results and create a false sense of security.

AI Discussion

Me: It's about a new system called Smishing Detector that can detect smishing SMS messages. It uses a neural network model to recognize certain patterns and keywords in messages.

Friend: That's really interesting. What are the implications of this system?

Me: Well, it could have a lot of implications in terms of reducing the number of smishing attacks. It could be used to protect people from fraud and scams, as well as to protect companies from malicious messages that could damage their reputation. It could also be used to help prevent cyberbullying and harassment.

Action items

Research and understand the concept of smishing and its implications.
Develop an understanding of the Smishing Detector model and its components.
Explore the potential of using the Smishing Detector model to detect smishing SMS in your own environment.

Technical terms

Smishing: Smishing is a type of phishing attack that uses SMS messages to deceive victims into providing personal information or downloading malicious software.
Neural Network: A neural network is a type of artificial intelligence system that is modeled after the human brain. It is composed of interconnected nodes that process information and can learn from experience.
Detection: Detection is the process of identifying the presence of an object or event. In the context of this article, it refers to the process of identifying smishing messages.
System: A system is a set of components that interact to achieve a common goal. In this article, the system refers to the SmishSMS system, which is designed to detect smishing messages.
SMS: SMS stands for Short Message Service and is a type of text messaging service used to send and receive messages on mobile phones.