Disrupting drive-by download networks on Twitter

Area of Science:

• Cybersecurity
• Social Network Analysis
• Computational Modeling

Background:

• Malicious actors exploit Twitter trends and hashtags to spread malware via drive-by download attacks.
• Twitter's character limits and URL shorteners facilitate rapid malware propagation.
• Existing disruption strategies struggle to effectively counter evolving cybercriminal tactics.

Purpose of the Study:

• To identify efficient network disruption strategies for combating malicious URLs on Twitter.
• To analyze the effectiveness of different parameters in disrupting malware propagation networks.
• To understand the role of network topology and resilience in cybersecurity defense.

Main Methods:

• Utilized computational modeling and social network analysis to study Twitter networks.
• Simulated disruption strategies targeting malicious URL propagation.
• Evaluated strategies based on network topology, resilience, and specific features.

Main Results:

• Disruption strategies focusing on network and emotion features are more effective than random removal.
• Targeted removal of malicious users enhances network vulnerability.
• Network resilience is a key factor in the long-term success of disruption efforts.

Conclusions:

• Strategic disruption of cybercriminal networks is essential for mitigating malware propagation.
• Understanding network dynamics is crucial for developing effective cybersecurity measures.
• Long-term cybersecurity requires adaptive and informed network intervention strategies.