Crowdsourcing is an information system for recruiting online workers to perform human intelligent tasks (HITs) that are hard for computers. Due to the openness of crowdsourcing, dynamic online workers with different knowledge backgrounds might give conflicting labels to a task. With the assumption that workers provide their labels independently, most existing works aggregate worker labels in a voting manner, which is vulnerable to Sybil attack where the attacker earns easy rewards by coordinating several Sybil workers to share a randomized label on each task for dominating the aggregation result. A strategic Sybil attacker also attempts to evade Sybil detection. In this paper, we propose a novel approach, called TDSSA (Truth Discovery against Strategic Sybil Attack), to defend against strategic Sybil attack. Experimental results on real-world and synthetic datasets indicate that TDSSA ensures more accurate inference of true labels under various Sybil attacking scenarios, as compared to state-of-the-art methods.