Internet of Things (IoT) is widely used in environmental monitoring, smart healthcare, and other fields. Due to its distributed nature, IoT is vulnerable to various internal attacks. One of these attacks is the packet-dropping attack, which is very harmful. The existing packet-dropping attack detection algorithms are unsuitable for emerging resource-constrained IoT networks. For example, ML-based algorithms always inject numerous packets to obtain the training dataset. However, it is heavyweight for energy-limited nodes to forward these extra packets. In this paper, we propose a lightweight evidence fusion-based detection algorithm (EFDA), which leverages the packet forwarding evidence to identify malicious nodes. Firstly, EFDA finds the sequence numbers of dropped packets and their corresponding source nodes. Then, it traces the routing path of each dropped packet and collects evidence for detection. The evidence stored by nodes around the path record the node’s forwarding behaviors. Finally, the collected evidence is fused to evaluate the trust of nodes. Based on nodes’ trust, the K-means clustering is used to distinguish between malicious nodes and benign nodes. We conduct simulation experiments to compare EFDA with ML-based algorithms. The experimental results demonstrate that EFDA can detect the packet-dropping attack without injecting packets and achieve a higher detection accuracy.