This paper proposes an intrusion detection system to prevent malicious node attacks that may result in failure links in wireless body area networks. The system utilizes a combination of Optimized Convolutional Neural Networks and Support Vector Machine techniques to classify nodes as malicious or not, and links as failure or not. In case of detection, the system employs a trust-based routing strategy to isolate malicious nodes or failure links and ensure a secure path. Furthermore, sensitive data is encrypted using a modified RSA encryption algorithm. The experimental results demonstrate the improved network performance in terms of data rate, delay, packet delivery ratio, energy consumption, and network security, by providing effective protection against malicious node attacks and failure links. The proposed system achieves the highest classification rate and sensitivity, surpassing similar methods in all evaluation metrics.