A Novel Supervised Deep Learning Solution to detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN)
Cybersecurity attacks are becoming increasingly sophisticated and pose a growing threat to individuals, and private and public sectors. DDoS attacks are one of the most harmful of these threats in today’s Internet, disrupting the availability of essential services. This project presents a novel deep learning-based approach for detecting DDoS attacks in network traffic using the industry-recognized CICDDoS2019 dataset, which contains packet captures from real-time DDoS attacks, creating a broader and more applicable model for the real world. The algorithm employed in this study exploits the properties of Convolutional Neural Networks (CNN) and common deep learning algorithms to build a novel mitigation technique that classifies benign and malicious traffic. The proposed model preprocesses the data by extracting packet flows and normalizing them to a fixed length. The data is then fed into a CNN architecture consisting of a 2D convolutional layer with 64 filters, kernel size of 3x3, and kernel regularization, followed by layers regulating node dropout, normalization, and a sigmoid activation function to out a binary classification. This will allow for the model to process the pcaps effectively and look for the nodes that contribute to DDoS attacks while dropping the “noise” or the distractors. The results of this study demonstrate the effectiveness of the proposed algorithm in detecting DDOS attacks in network traffic as well as being scalable for any network environment.
Received Best of Fair Award at Austin Regional Science Fair as well as US Air Force Award and Navy Award for Outstanding Research.