<p>This article studies attack detection problems for the secure distributed state estimation of multi-sensor networks with intermittent observation. The Kalman consensus filter is equipped to develop the minimum mean square error estimation of the process. Due to the vulnerability of the communication network, an attack scenario is considered in which both the sensor-to-estimator channels and the estimator-to-estimator channels are attacked. The attackers would intercept and modify the measurement based on a linear attack strategy. Meanwhile, the false data are injected into the prior state estimates sent to other nodes. The <i>χ</i>² detectors fail to identify the well-designed linear and false data injection attacks. To overcome this drawback, a watermarking-based attack detection strategy is proposed. The effectiveness of the proposed scheme for stealthy attacks is analyzed. Furthermore, the presence of intermittent observations prevents the residuals from reflecting false data injection attacks in estimator-to-estimator channels. This problem is effectively addressed by employing a stochastic detection strategy integrated with watermarking. Based on effective attack detection, malicious data can be mitigated using the Kalman consensus filter to avoid performance degradation. Finally, a numerical simulation validates the effectiveness of the proposed method.</p>