Abstract: In order to safeguard “Wireless Sensor Networks (WSNs),” this research suggests a new rule-based intrusion detection architecture that includes a number of security techniques. The framework employs a set of rules generated by the Random Forest algorithm, which outperforms other machine learning algorithms in terms of detection accuracy, false positive rate, and overhead. The generated rules are based on features extracted from WSNs data, including packet size, energy consumption, and hop count. The framework’s performance is evaluated using a set of metrics, demonstrating its effectiveness in identifying numerous attack kinds. Combining rule-based systems and machine learning approaches in this proposed framework can improve the accuracy and efficiency of intrusion detection mechanisms for secure WSNs. The proposed framework can be a valuable addition to the existing intrusion detection mechanisms for secure WSNs.

Intrusion Detection System, Wireless Sensor Networks, Security Attacks, Machine Learning,Base Station,Security threat.