Improving network resilience against DDoS attacks: A fuzzy TOPSIS-based quantitative assessment approach

Area of Science:

• Computer Science
• Information Security
• Network Engineering

Background:

• The explosive growth of the internet and digital platforms generates vast amounts of data daily, increasing the importance of network security.
• Safeguarding sensitive data requires effective information security measures against diverse threats due to expanding user bases.
• Key network security attributes include message encryption, password security, wireless network protection, and vulnerability detection.

Purpose of the Study:

• To systematically evaluate network security resilience, specifically against Distributed Denial of Service (DDoS) attacks, using a Multi-Criteria Decision Making (MCDM) approach.
• To assess various security attributes and vulnerabilities to provide actionable insights for strengthening organizational cybersecurity frameworks.
• To inform targeted actions for addressing vulnerabilities and enhancing resource protection.

Main Methods:

• Utilized a fuzzy TOPSIS-based method, a type of Multi-Criteria Decision Making (MCDM).
• Conducted network security audits to identify vulnerabilities impacting commercial operations and sensitive data.
• Evaluated network resilience against simulated DDoS attacks.

Main Results:

• Network N6 demonstrated the highest security and resilience against DDoS attacks.
• The ranking of network security resilience was N6 > N1 > N5 > N3 > N4 > N2.
• Identified critical vulnerabilities and provided insights for enhancing cybersecurity frameworks.

Conclusions:

• The fuzzy TOPSIS MCDM approach effectively evaluates network security resilience against complex cyber threats like DDoS attacks.
• The findings support strategic decision-making to strengthen network defenses and improve overall cybersecurity.
• This research contributes to organizational efforts in safeguarding data, employees, and customer information from evolving cyber threats.