AI-DRIVEN VULNERABILITY ANALYSIS IN  NETWORK PROTOCOL SECURITY

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RIANNA SARA THOMAS

AI-DRIVEN VULNERABILITY ANALYSIS IN NETWORK PROTOCOL SECURITY

Authors: RIANNA SARA THOMAS

Unique Paper ID: 183410
Volume: 12
Issue: 3
PageNo: 1337-1340

Keywords: AI-driven security anomaly detection automated threat mitigation cybersecurity machine learning network protocols vulnerability analysis.

Abstract:
With the increasing complexity of network protocols, cybersecurity threats continue to evolve, exploiting vulnerabilities that traditional security mechanisms struggle to detect. Artificial Intelligence (AI) has emerged as a powerful tool for enhancing vulnerability analysis in network protocol security. This research explores AI-driven approaches to identifying, assessing, and mitigating vulnerabilities in network protocols, focusing on machine learning (ML) and deep learning (DL) techniques for automated threat detection and response. The study examines how AI can analyse network traffic patterns, detect anomalies, and predict potential security breaches with higher accuracy and efficiency than conventional methods. Key AI techniques, including supervised and unsupervised learning, neural networks, and reinforcement learning, are evaluated for their effectiveness in identifying zero-day vulnerabilities and protocol-based attacks such as man-in-the-middle (MITM), denial-of-service (DoS), and protocol downgrades. Additionally, the research investigates AI-driven penetration testing and automated vulnerability assessment frameworks that enhance proactive security measures. Despite its advantages, AI-based vulnerability analysis presents challenges, including adversarial AI attacks, data privacy concerns, and computational overhead. This paper discusses these limitations and proposes strategies for enhancing the reliability and robustness of AI-driven security solutions. By integrating AI with cybersecurity frameworks, organizations can significantly strengthen their network defense mechanisms, reduce attack surfaces, and enhance real-time threat mitigation. The findings contribute to the advancement of AI applications in cybersecurity and highlight future research directions in securing network protocols against evolving cyber threats.

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Cite This Article

ISSN: 2349-6002
Volume: 12
Issue: 3
PageNo: 1337-1340

AI-DRIVEN VULNERABILITY ANALYSIS IN NETWORK PROTOCOL SECURITY

Available:https://ijirt.org/article?manuscript=183410

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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