The increase and professionalization of cyberattacks calls for the development of relevant defense-in-depth mechanisms of which intrusion detection systems (IDSs) are essential components. This paper provides an in-depth analysis of system call-based IDSs as intelligence for detecting malicious activities. A systematic analysis of 209 publications from the scientific literature between 1996 and early 2026 highlights trends in this field of research and defines a taxonomy presenting the different approaches proposed by researchers. Eighteen state-of-the-art methods, representative of the diversity of approaches proposed in the literature, were reproduced and evaluated on two public datasets, ADFA-LD and NGIDS-DS. The detection performance and overhead of each method are examined in great detail, opening discussions on the shortcomings of the state of the art, limitations of system call-based IDSs, and lines of research that would enable this type of detection system to meet the challenges of deployment in a real-world environment. Finally, recommendations for future work are derived from these findings.
Arnoud et al. (Sat,) studied this question.