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Cybersecurity has become a primary global concern with the rapid increase in security attacks and data breaches. Artificial intelligence is promising to help humans analyzing and identifying attacks. However, labeling millions of packets for supervised learning is never easy. This study aims to leverage transfer learning technique that stores the knowledge gained from well-defined attack lifecycle documents and applies it to hundred thousands of unlabeled attacks (packets) for identifying their attack tactics. We anticipate the knowledge of an attack is well-described in the documents, and the cutting edge transformer-based language model can embed the knowledge into a high-dimensional latent space. Then, reusing the information from the language model for the learning of attack tactic carried by packets to improve the learning efficiency. We propose a system, PELAT, that fine-tunes BERT model with 1,417 articles from MITRE ATT&CK lifecycle framework to enhance its attack knowledge (including syntax used and semantic meanings embedded). PELAT then transfers its knowledge to perform semi-supervised learning for unlabeled packets to generate their tactic labels. Further, when a new attack packet arrives, the packet payload will be processed by the PELAT language model with a downstream classifier to predict its tactics. In this way, we can effectively reduce the burden of manually labeling big datasets. In a one-week honeypot attack dataset (227 thousand packets per day), PELAT performs 99% of precision, recall, and F1 on testing dataset. PELAT can infer over 99% of tactics on two other testing datasets (while nearly 90% of tactics are identified).