Research on flow table overflow attacks in software defined networks based on machine learning

Puntos clave

• This research aims to investigate flow table overflow attacks on software defined networks leveraging machine learning techniques.
• Analyzed the types of flow table overflow attacks.
• Applied machine learning algorithms to detect and mitigate these attacks.
• Conducted simulations to evaluate the performance of the proposed approaches.
• The machine learning methods effectively identified flow table overflow attacks.
• Enhanced security protocols were developed to counteract the attacks, improving network integrity.
• Simulation results indicated a significant reduction in attack success rates.