  As the internet continues to be populated with new devices and emerging
technologies, the attack surface grows exponentially. Technology is shifting
towards a profit-driven Internet of Things market where security is an
afterthought. Traditional defending approaches are no longer sufficient to
detect both known and unknown attacks to high accuracy. Machine learning
intrusion detection systems have proven their success in identifying unknown
attacks with high precision. Nevertheless, machine learning models are also
vulnerable to attacks. Adversarial examples can be used to evaluate the
robustness of a designed model before it is deployed. Further, using
adversarial examples is critical to creating a robust model designed for an
adversarial environment. Our work evaluates both traditional machine learning
and deep learning models' robustness using the Bot-IoT dataset. Our methodology
included two main approaches. First, label poisoning, used to cause incorrect
classification by the model. Second, the fast gradient sign method, used to
evade detection measures. The experiments demonstrated that an attacker could
manipulate or circumvent detection with significant probability.
