A Semisupervised Majority Weighted Vote Antiphishing Attacks IDS for the Education Industry

Area of Science:

• Computer Science
• Cybersecurity
• Information Technology

Background:

• Digital transformation increases vulnerability to phishing attacks.
• The education sector is a prime target due to increased online learning.
• Lack of user awareness and inadequate security measures exacerbate phishing risks.

Purpose of the Study:

• To develop and evaluate a semisupervised system for detecting phishing attacks.
• To address the specific challenges of phishing in the education sector.
• To enhance the reliability of phishing detection in complex digital environments.

Main Methods:

• Implementation of a semisupervised majority-weighted vote system.
• Utilizing a realistic majority weighted vote scheme for classifier selection.
• Development of a voting naive Bayes positive algorithm for probabilistic learning.

Main Results:

• The proposed system demonstrates high reliability in detecting phishing attacks.
• The voting naive Bayes positive algorithm accurately predicts phishing attempts.
• The system is optimized for complex decision-making environments in cybersecurity.

Conclusions:

• The semisupervised majority-weighted vote system offers an effective solution for phishing detection in the education sector.
• The innovative voting naive Bayes positive algorithm enhances probabilistic learning for improved accuracy.
• This research contributes to securing online educational environments against sophisticated phishing threats.