A variable-trust threshold-based approach for DDOS attack mitigation in software defined networks.

Area of Science:

• Computer Science
• Network Security
• Cybersecurity

Background:

• Software-Defined Networks (SDN) offer advanced features but are vulnerable to attacks, particularly Distributed Denial of Service (DDoS).
• The centralized nature of SDN controllers makes them a critical target for DDoS attacks, potentially crippling the entire network.
• Existing DDoS mitigation schemes struggle with accurately distinguishing legitimate from malicious traffic.

Purpose of the Study:

• To propose and evaluate a novel two-phase algorithm for mitigating DDoS attacks targeting SDN controllers.
• To enhance the security and reliability of SDN by effectively detecting and mitigating malicious traffic.
• To address the challenge of dynamically determining thresholds for differentiating legitimate and malicious user requests.

Main Methods:

• A two-phase algorithm is introduced for DDoS attack detection in SDN.
• Phase 1 involves extracting relevant header fields from network traffic.
• Phase 2 calculates a dynamic trust value for each user based on header information, enabling adaptive threat detection.

Main Results:

• The proposed approach demonstrates superior performance compared to existing DDoS detection schemes.
• Achieved high accuracy, detection rate, and a low false-positive rate in identifying malicious traffic.
• The system's accuracy reached up to 98.83%, significantly outperforming traditional methods.

Conclusions:

• The developed two-phase algorithm effectively mitigates DDoS attacks in SDN environments.
• Dynamic trust value calculation provides a more robust method for distinguishing malicious from legitimate requests.
• The findings highlight a significant advancement in securing SDN infrastructure against sophisticated cyber threats.