  Due to the numerous advantages of machine learning (ML) algorithms, many
applications now incorporate them. However, many studies in the field of image
classification have shown that MLs can be fooled by a variety of adversarial
attacks. These attacks take advantage of ML algorithms' inherent vulnerability.
This raises many questions in the cybersecurity field, where a growing number
of researchers are recently investigating the feasibility of such attacks
against machine learning-based security systems, such as intrusion detection
systems. The majority of this research demonstrates that it is possible to fool
a model using features extracted from a raw data source, but it does not take
into account the real implementation of such attacks, i.e., the reverse
transformation from theory to practice. The real implementation of these
adversarial attacks would be influenced by various constraints that would make
their execution more difficult. As a result, the purpose of this study was to
investigate the actual feasibility of adversarial attacks, specifically evasion
attacks, against network-based intrusion detection systems (NIDS),
demonstrating that it is entirely possible to fool these ML-based IDSs using
our proposed adversarial algorithm while assuming as many constraints as
possible in a black-box setting. In addition, since it is critical to design
defense mechanisms to protect ML-based IDSs against such attacks, a defensive
scheme is presented. Realistic botnet traffic traces are used to assess this
work. Our goal is to create adversarial botnet traffic that can avoid detection
while still performing all of its intended malicious functionality.
