—Machine-to-machine (M2M) communication is one of the leading facilitators of the Internet of Things environment by offering ubiquitous applications and services. Using cellular networks for providing M2M connectivity brings several advantages such as extended coverage, security, robust management, and lower deployment costs; however, coexistence with a large number of M2M devices is still an essential challenge in LTE-A networks, in part due to the difficulty in allowing simultaneous access. Although the random access procedure in LTE-A is adequate for human-to-human (H2H) communications, when M2M communications are involved congestion control is required. One of the congestion control schemes suggested by the 3GPP is Access Class Barring (ACB), which can reduce the number of simultaneous users contending for access. However, it is still not clear how to adapt ACB parameters in bursty and heavy-loaded scenarios, such as those that appear in M2M communications. We propose a dynamic ACB scheme in which an estimate of the current number of M2M devices in backoff state is used to adjust in real-time the barring rate parameter. We evaluate the key performance indicators (KPIs) of dynamic ACB in several scenarios with different degrees of traffic load and compare them with those of a static ACB with optimal parameters. We show that the dynamic ACB outperforms the static one offering shorter access delay and higher successful access probability, while its impact on H2H communications KPIs is negligible. Besides, our proposed scheme conforms with the LTE-A specification so that it can be included as a viable solution.