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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Risk mitigation approach to cyber threat using ai-driven models

Risk mitigation approach to cyber threat using ai-driven models

Olanrewaju, Jesufemi
Togunde, Matthias Oluloni

Paperback

General Computers

ISBN10: 6208432413
ISBN13: 9786208432416
Publisher: LAP Lambert Academic Publishing
Published: Feb 26 2025
Pages: 76
Weight: 0.25
Height: 0.18 Width: 6.00 Depth: 9.00
Language: English
This systematic review evaluates the effectiveness of AI-driven models in mitigating evolving cyber threats, focusing on machine learning techniques like supervised, unsupervised, and deep learning. Deep learning excels in detecting complex threats like APTs and zero-day vulnerabilities, while supervised learning is effective for known threats but struggles with novel attacks. Unsupervised learning adapts well to dynamic environments but has higher false positive rates. The review proposes a multi-layered framework combining AI models with traditional security measures for enhanced threat detection and response. Challenges such as data quality, algorithmic bias, and adversarial attacks must be addressed for optimal implementation. A hybrid approach is recommended for robust cybersecurity.

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