CyberEduPlatform: an educational tool to improve cybersecurity through anomaly detection with Artificial Intelligence

Area of Science:

• Cybersecurity
• Artificial Intelligence
• Network Traffic Analysis
• Information Security Education

Background:

• The digital era faces escalating cyber threats, impacting individuals and organizations.
• Traditional security measures are challenged by the sophistication and volume of cyberattacks.
• University networks are vulnerable targets requiring advanced protection strategies.

Purpose of the Study:

• To evaluate artificial intelligence (AI) for detecting network traffic anomalies in a university setting.
• To assess the efficacy of automated detection of unusual network activities.
• To emphasize the role of cybersecurity awareness and education in threat mitigation.

Main Methods:

• Extensive simulations were conducted to test AI model performance.
• Advanced artificial intelligence models were employed for anomaly detection.
• A cybersecurity education platform, CyberEduPlatform, was introduced to enhance user awareness.

Main Results:

• AI models demonstrated high precision in identifying network anomalies and unconventional activities.
• Simulations confirmed the potential of AI in real-time threat detection.
• CyberEduPlatform showed promise in improving users' understanding of cyber risks.

Conclusions:

• An integrated approach combining AI-driven anomaly detection and cybersecurity education is essential.
• AI enhances network security, but human awareness remains a critical component of defense.
• Continuous development of both technological solutions and educational strategies is necessary for comprehensive cybersecurity.