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An Advanced Deep Attention Collaborative Mechanism for Secure Educational Email Services.

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This study introduces a novel deep attention collaborative filter to combat spam in academic email services. The advanced algorithm effectively detects and categorizes malicious emails, enhancing educational cybersecurity.

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Area of Science:

  • Cybersecurity
  • Information Technology
  • Educational Technology

Background:

  • The COVID-19 pandemic increased reliance on digital education, exposing vulnerabilities in cybersecurity for educational institutions.
  • E-learning systems, content services, and trainee management systems face escalating cyber threats, necessitating advanced security solutions.
  • Academic email systems are daily targets for spam, phishing, and malware, requiring robust protection.

Purpose of the Study:

  • To develop an advanced deep attention collaborative filter for secure academic email services.
  • To model and address the problem of email spam as a graph-based system using intelligent techniques.
  • To enhance the security of digital communication within educational organizations.

Main Methods:

  • Utilized a deep attention collaborative filter for secure academic email services.
  • Modeled spam detection as a graph system, employing collaborative referral systems for social information analysis.
  • Applied nonnegative matrix factorization (NMF) on social graph adjacency tables to identify user communities.
  • Integrated deep attention mechanisms for user personalization and exponential random graph models (ERGMs) to mitigate local dependencies and reveal malicious communities.

Main Results:

  • The proposed algorithm successfully detects and categorizes spam emails by analyzing social information within a graph framework.
  • Nonnegative matrix factorization (NMF) effectively grouped users into overlapping communities.
  • Deep attention mechanisms provided user-specific filtering, while ERGMs helped identify malicious community structures.
  • The methodology demonstrated successful implementation in educational email protection systems.

Conclusions:

  • The developed deep attention collaborative filter offers a specialized and effective solution for securing academic email services.
  • The graph-based modeling approach, combined with NMF, deep attention, and ERGMs, significantly improves spam detection and classification.
  • The proposed algorithm outperforms existing methods in protecting educational institutions from email-based cyber threats.