  Cybersecurity attacks are becoming increasingly sophisticated and pose a
growing threat to individuals, and private and public sectors. Distributed
Denial of Service 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 DDoS evaluation
dataset from the University of New Brunswick, 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 which is fed into a custom architecture
containing layers regulating node dropout, normalization, and a sigmoid
activation function to out a binary classification. This allows for the model
to process the flows 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, achieving an accuracy of .9883 on 2000 unseen flows in network
traffic, while being scalable for any network environment.
