In the past few years, Convolutional Neural Networks (CNN) have demonstrated
promising performance in various real-world cybersecurity applications, such as
network and multimedia security. However, the underlying fragility of CNN
structures poses major security problems, making them inappropriate for use in
security-oriented applications including such computer networks. Protecting
these architectures from adversarial attacks necessitates using security-wise
architectures that are challenging to attack.

In this study, we present a novel architecture based on an ensemble
classifier that combines the enhanced security of 1-Class classification (known
as 1C) with the high performance of conventional 2-Class classification (known
as 2C) in the absence of attacks.Our architecture is referred to as the
1.5-Class (SPRITZ-1.5C) classifier and constructed using a final dense
classifier, one 2C classifier (i.e., CNNs), and two parallel 1C classifiers
(i.e., auto-encoders). In our experiments, we evaluated the robustness of our
proposed architecture by considering eight possible adversarial attacks in
various scenarios. We performed these attacks on the 2C and SPRITZ-1.5C
architectures separately. The experimental results of our study showed that the
Attack Success Rate (ASR) of the I-FGSM attack against a 2C classifier trained
with the N-BaIoT dataset is 0.9900. In contrast, the ASR is 0.0000 for the
SPRITZ-1.5C classifier.

By admin