Employing machine learning based malicious signal detection for cognitive radio networks

In cognitive radio networks, the empty spectrum that is also named as spectrum hole is detected with the help of spectrum sensing techniques. Energy detection is the most utilized spectrum sensing technique owing to its low complexity. In the energy detection technique, a spectrum hole is detected with a predefined threshold. In this article, machine learning based malicious signal detection is employed for cognitive radio networks. The design of cognitive radio users and network environment is simulated with Riverbed simulation software. The received signal is controlled whether it is a malicious signal or just a secure sensing signal. The fuzzy logic based system is utilized for the security categorization of spectrum sensing signals as malicious, suspicious, and secure sensing signals. Fuzzy logic parameters are taken from the machine learning features that are chosen as the most effective 3 features among all 49 features. The security of primary users is enhanced when compared to other schemes in the literature. The results of the proposed machine learning based malicious signal detection system are validated with the results acquired from the fuzzy logic based approach. The random forest method gives the best results among all machine learning methods for the detection of signals.

Automated summary

In this article, a machine learning based malicious signal detection system is proposed for cognitive radio networks. The system utilizes fuzzy logic for the security categorization of spectrum sensing signals and is validated with the results obtained from a fuzzy logic based approach. The random forest method performs the best among all machine learning methods for signal detection.