  The rapid advancement of artificial intelligence within the realm of
cybersecurity raises significant security concerns. The vulnerability of deep
learning models in adversarial attacks is one of the major issues. In
adversarial machine learning, malicious users try to fool the deep learning
model by inserting adversarial perturbation inputs into the model during its
training or testing phase. Subsequently, it reduces the model confidence score
and results in incorrect classifications. The novel key contribution of the
research is to empirically test the black-box adversarial transferability
phenomena in cyber attack detection systems. It indicates that the adversarial
perturbation input generated through the surrogate model has a similar impact
on the target model in producing the incorrect classification. To empirically
validate this phenomenon, surrogate and target models are used. The adversarial
perturbation inputs are generated based on the surrogate-model for which the
hacker has complete information. Based on these adversarial perturbation
inputs, both surrogate and target models are evaluated during the inference
phase. We have done extensive experimentation over the CICDDoS-2019 dataset,
and the results are classified in terms of various performance metrics like
accuracy, precision, recall, and f1-score. The findings indicate that any deep
learning model is highly susceptible to adversarial attacks, even if the
attacker does not have access to the internal details of the target model. The
results also indicate that white-box adversarial attacks have a severe impact
compared to black-box adversarial attacks. There is a need to investigate and
explore adversarial defence techniques to increase the robustness of the deep
learning models against adversarial attacks.
