The open nature of wireless media poses a challenge for information security. The Time Division Multiple Access (TDMA) protocol is a predominant protocol tailored for time-sensitive industrial applications. Considering the time-slot scheduling characteristics of TDMA-based wireless sensor networks, this study proposes two types of masquerade attack models: an idle time-slot attack model and a retransmission time-slot attack model. In response to these two attack models and starting from the inherent transmission features of TDMA wireless sensor networks while considering their periodic transmission pattern and the fundamental transmission unit being a single time slot, a high-precision intrusion detection method based on fine-grained temporal feature extraction is proposed. First, fine-grained temporal features are extracted in the time dimension by leveraging information such as packet reception time and superframe start time to calculate the positional information of the transmission time slot. Subsequently, the positional information is fed into the Isolation Forest (IF)-an unsupervised learning model-for training and learning. Finally, a legitimacy assessment is conducted on two data packets received from the same node within one superframe cycle that have identical sequence numbers. The experimental results demonstrate that the two proposed masquerade attacks can evade existing intrusion detection methods and the proposed intrusion detection approach can effectively detect these two masquerade attacks. Compared to traditional methods, this approach achieves a 14.5% increase in the detection success rate when the packet loss rate is 30%.