Modified light GBM based classification of malicious users in cooperative cognitive radio networks

ABSTRACT

This paper proposes the Modified Light GBM to classify the Malicious Users (MUs) and legitimate Secondary Users (SUs) in the cognitive-radio network. The proposed method is to avoid the consequences of malicious users in Cooperative Spectrum Sensing (CSS) without the detection of MUs. The method is tested against the occurrence of Always No Malicious User (ANMU), always yes malicious user (AYMU), the Random Malicious User (RMU), and Opposite Malicious User (OMU) transmitting spectrum sensing data to the Fusion Center (FC) by normal secondary users. The efficiency of the proposed method is expressed via simulations and compared with other existing methods.

KEYWORDS:

• Cooperative spectrum sensing
• cognitive radio network
• malicious user
• fusion centre
• secondary user
• false alarm rate
• LightGBM
• signal to noise ratio
• Hyper-parameter optimisation

Disclosure statement

No potential conflict of interest was reported by the author(s).