  In the past few years, cybersecurity is becoming very important due to the
rise in internet users. The internet attacks such as Denial of service (DoS)
and Distributed Denial of Service (DDoS) attacks severely harm a website or
server and make them unavailable to other users. Network Monitoring and control
systems have found it challenging to identify the many classes of DoS and DDoS
attacks since each operates uniquely. Hence a powerful technique is required
for attack detection. Traditional machine learning techniques are inefficient
in handling extensive network data and cannot extract high-level features for
attack detection. Therefore, an effective deep learning-based intrusion
detection system is developed in this paper for DoS and DDoS attack
classification. This model includes various phases and starts with the Deep
Convolutional Generative Adversarial Networks (DCGAN) based technique to
address the class imbalance issue in the dataset. Then a deep learning
algorithm based on ResNet-50 extracts the critical features for each class in
the dataset. After that, an optimized AlexNet-based classifier is implemented
for detecting the attacks separately, and the essential parameters of the
classifier are optimized using the Atom search optimization algorithm. The
proposed approach was evaluated on benchmark datasets, CCIDS2019 and UNSW-NB15,
using key classification metrics and achieved 99.37% accuracy for the UNSW-NB15
dataset and 99.33% for the CICIDS2019 dataset. The investigational results
demonstrate that the suggested approach performs superior to other competitive
techniques in identifying DoS and DDoS attacks.
