  In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a
promising future architecture in terms of minimizing energy consumption and
allocating resources efficiently by providing real-time cloud infrastructures,
cooperative radio, and centralized data processing. Recently, given their
vulnerability to malicious attacks, the security of C-RAN networks has
attracted significant attention. Among various anomaly-based intrusion
detection techniques, the most promising one is the machine learning-based
intrusion detection as it learns without human assistance and adjusts actions
accordingly. In this direction, many solutions have been proposed, but they
show either low accuracy in terms of attack classification or they offer just a
single layer of attack detection. This research focuses on deploying a
multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN
that can detect and classify four types of jamming attacks: constant jamming,
random jamming, deceptive jamming, and reactive jamming. This deployment
enhances security by minimizing the false negatives in C-RAN architectures. The
experimental evaluation of the proposed solution is carried out using WSN-DS
(Wireless Sensor Networks DataSet), which is a dedicated wireless dataset for
intrusion detection. The final classification accuracy of attacks is 94.51\%
with a 7.84\% false negative rate.
