Some of the modern smart grid infrastructures, phasor measurement units (PMUs) for instance, are vulnerable to cyberattacks due to their ever-increasing dependence on information and communications technologies. In general, existing solutions to cyberattacks focus on creating redundancy and/or enhancing security levels of sensing and communication networks. These solutions require intensive offline efforts and therefore are economically expensive. Further, they are generally inefficient when dealing with dynamic attacks. This paper proposes a novel density-based spatial clustering approach for online detection, classification, and data recovery for data manipulation attacks to PMU measurements. The proposed method is purely data-driven and is applicable to simultaneous multi-measurement attacks without requiring additional hardware in the existing infrastructure. The proposed approach is also independent of the conventional state estimation. Comprehensive case studies demonstrate the effectiveness of the proposed method.