This repository documents the collaborative efforts of the Undergraduate Research group under the supervision of Prof. Brian Ricks. The primary objective of this research initiative is to actively contribute to cutting-edge research in the field of network security. Our focus is on the development of innovative techniques to detect and mitigate Distributed Denial-of-Service (DDoS) attacks and DDoS-as-a-Smokescreen (DaaSS) threats.
Detection Enhancement: Our main goal is to enhance the detection capabilities for both DDoS attacks and DDoS-as-a-Smokescreen threats.
Reduced Detection Times: We aim to design and implement a novel approach that utilizes segmented NetFlow data and unsupervised machine learning methods. This approach is intended to reduce DDoS and DaaSS detection times from minutes to seconds.
Maintaining Classification Performance: While reducing detection times, our objective is to maintain a strong classification performance, ensuring accurate identification of malicious activities.
False Positive Reduction: Another crucial aspect of our research is to reduce potential false positive percentages, enhancing the reliability of the detection system.