SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection. (arXiv:2305.00550v1 [cs.CR])

Machine Learning (ML) has become a valuable asset to solve many real-world
tasks. For Network Intrusion Detection (NID), however, scientific advances in
ML are still seen with skepticism by practitioners. This disconnection is due
to the intrinsically limited scope of research papers, many of which primarily
aim to demonstrate new methods ``outperforming'' prior work -- oftentimes
overlooking the practical implications for deploying the proposed solutions in
real systems. Unfortunately, the value of ML for NID depends on a plethora of …

aim assessment asset detection intrusion intrusion detection machine machine learning network papers research scope world