Codes of “Comparative Analysis of Feature Selection Techniques for Malicious Website Detection in SMOTE”

40 £

This is the full Simulation Codes package used to generate the results presented in the paper titled “Comparative Analysis of Feature Selection Techniques for Malicious Website Detection in SMOTE Balanced Data”. Article link: https://doi.org/10.46470/03d8ffbd.993cf635.

  Contact us

Description

This is the full Simulation Codes package used to generate the results presented in the paper titled “Comparative Analysis of Feature Selection Techniques for Malicious Website Detection in SMOTE Balanced Data”. Article link: https://doi.org/10.46470/03d8ffbd.993cf635 

Article link: https://rs-ojict.pubpub.org/pub/ci8qmhls

====== Work Summary =====

The advancement in network technology has led to an exponential rise in the number of internet users across the globe. The increase in internet usage has resulted in an increase in both the number of malicious websites and cybercrimes reported over the years. Therefore, it has become critical to devise an intelligent solution that can detect malicious websites and be used in real-time systems. In our paper, we perform a comparative analysis of various feature selection techniques to build a time-efficient and accurate predictive model. To build our predictive model, a set of features are selected by feature selection methods. The selected features consist of at least 70% of the categorical features in all feature selection techniques examined in this paper. Keeping the end goal of real-time deployment of models in context the cost of processing or storing these features is far cheaper when compared to text or image-based features. We started out with a class imbalance in our data which was later dealt with using the Synthetic Minority Oversampling Technique. Our proposed model also bested the existing work in the literature when compared over various evaluation metrics. The result indicated that Embedded feature selection was the best technique considering the accuracy of the model. The Filter-based technique may also be used in the context of developing a low latency system at the cost of the accuracy of the model.

 

Q & A

There are no questions yet

Ask a question

Your question will be answered by a store representative or other customers.

Thank you for the question!

Mail

Your question has been received and will be answered soon. Please do not submit the same question again.

Error

Warning

An error occurred when saving your question. Please report it to the website administrator. Additional information:

Add an answer

Thank you for the answer!

Mail

Your answer has been received and will be published soon. Please do not submit the same answer again.

Error

Warning

An error occurred when saving your question. Please report it to the website administrator. Additional information:

Refund Policy

We firmly believe in and stand behind our product 100%, but we understand that it might not meet the needs of everyone all of the time. If you are unhappy with your purchase, or you have an issue that we are unable to resolve that makes the system unusable, we will be happy to consider offering a refund.
  • Refunds will be offered at our sole discretion and must meet all of the following conditions fully:
  • You are within the first 24 hours of the purchase of the product.
  • Your issue(s) comes from not being able to install the product properly or get the product to perform its basic functions.
  • You have attempted to resolve your issue(s) with our support team.
  • No refunds will be granted after the first 24 hours of the original purchase whatsoever.
  • Issues caused by 3rd party products, or other software will not provide grounds for a refund.
  • Refund requests for renewal subscription orders will not be entertained.

*PS: RS Products’s purchased with discount or sale offer are non-refundable

General Inquiries

There are no inquiries yet.