This paper reviews the problem of ensuring cyber resilience for digital substations, critical energy infrastructure facilities vulnerable to targeted cyberattacks. A solution is proposed in the form of a comprehensive technology integrating intelligent attack and anomaly detection using a radial-basis neural network and an adaptive control mechanism built on a stochastic self-learning machine with a variable structure and linear tactics. The detector ensures resource-efficient and highly accurate detection of cyberattacks on low-resource devices of the digital substation network at the connection level. The adaptive control mechanism dynamically restructures the digital substation network’s flows based on environmental responses and undergoes additional learning during incident processing, enabling the neutralization of a wide range of known and unknown threats. Experimental results have demonstrated that the proposed solution meets key requirements for digital substation protection systems: high performance, adaptability, and cybersecurity.