  Convolutional neural networks (CNNs) models play a vital role in achieving
state-of-the-art performances in various technological fields. CNNs are not
limited to Natural Language Processing (NLP) or Computer Vision (CV) but also
have substantial applications in other technological domains, particularly in
cybersecurity. The reliability of CNN's models can be compromised because of
their susceptibility to adversarial attacks, which can be generated
effortlessly, easily applied, and transferred in real-world scenarios.
  In this paper, we present a novel and comprehensive method to improve the
strength of attacks and assess the transferability of adversarial examples in
CNNs when such strength changes, as well as whether the transferability
property issue exists in computer network applications. In the context of our
study, we initially examined six distinct modes of attack: the Carlini and
Wagner (C&W), Fast Gradient Sign Method (FGSM), Iterative Fast Gradient Sign
Method (I-FGSM), Jacobian-based Saliency Map (JSMA), Limited-memory Broyden
fletcher Goldfarb Shanno (L-BFGS), and Projected Gradient Descent (PGD) attack.
We applied these attack techniques on two popular datasets: the CIC and UNSW
datasets. The outcomes of our experiment demonstrate that an improvement in
transferability occurs in the targeted scenarios for FGSM, JSMA, LBFGS, and
other attacks. Our findings further indicate that the threats to security posed
by adversarial examples, even in computer network applications, necessitate the
development of novel defense mechanisms to enhance the security of DL-based
techniques.
