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In this article, a machine learning technique is proposed for the detection of cyber-attacks in Photovoltaic (PV) farms using point of common coupling (PCC) sensors alone. A comprehensive cyber-attack model of a PV farm is first developed to consider operating conditions variability. The attack model specifically includes two types of cyber-attacks that are historically more difficult to detect. A Convolutional Neural Network (CNN) using <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>PMU plus figures of merit is proposed and compared with other machine learning techniques using raw electric waveform and micro-phase measurement units (<inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>PMU), respectively. Finally, a cyber-physical security testbed of an IEEE 37-bus distributed grid with PV farms is developed. A real-time simulation, detection, and visualization framework is designed to demonstrate the feasibility of the proposed method in a real-world application. Results show that the proposed machine learning methods can achieve adequate detection accuracy and robustness under various attack scenarios.