A new machine learning system has been developed to autonomously detect and mitigate cyberattacks in real-time within software-defined networks. This system, detailed in a recent arXiv paper, aims to address the increasing speed of cyber threats, where adversaries can move laterally within networks in minutes or even seconds. It comprises two modules: one for creating and preprocessing network datasets, and another for automating the training and evaluation of algorithms to trigger blocking actions. A case study demonstrated the system successfully detecting and blocking a SYN flooding denial-of-service attack in 21 seconds without human intervention. AI
IMPACT This system could significantly reduce response times to cyber threats, potentially mitigating damage from fast-moving attacks.
RANK_REASON The cluster contains a research paper published on arXiv detailing a new machine learning system for cyber defense. [lever_c_demoted from research: ic=1 ai=1.0]
- Alexandre Amaral de Aguiar
- arXiv
- Intrusion Prevention System
- machine learning
- Network Dataset Creation
- Software Defined Networks in Wireless Sensor Architectures
- SYN flooding denial of service attack
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