2026-06-02T21:45:08Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/453592026-02-12T00:45:47Zcom_10630_2254col_10630_37953

00925njm 22002777a 4500 dc Elicha, Jacobo author López-Muñoz, Francisco Javier author 2025 The ever-increasing number of cyber-attacks poses a serious challenge to incident response teams. Recent cyber-attacks, such as the attack against energy distribution companies in Ukraine, highlight the disruption which can be caused and its consequences. More than 53 % of recorded incidents targeted essential entities, which heavily rely in Industrial Internet of Things (IIoT) devices, according to ENISA. Despite the amount of work in Cyber Threat Intelligence (CTI) and Intrusion Detection Systems (IDSs), automated response systems have been avoided in connected industrial environments mainly due to the criticality of the underlying assets, where a misstep has the potential to result in the disruption of critical processes. This paper therefore presents an Early and Adaptive Automated Intrusion Response Service for industrial environments, named EAIRS, which combines several techniques, including expert systems and reinforcement learning, to classify and mitigate anomalies detected by IDSs. The incidents EAIRS is designed to face range from network to host-based attacks. This paper provides the architecture for the described approach and the evaluation of a proof-of-concept implementation on an experimental testbed. Jacobo Elicha, Javier Lopez, Dispatching advanced and adaptive intrusion responses for IIoT-based systems, Future Generation Computer Systems, Volume 179, 2026, 108314, ISSN 0167-739X, https://doi.org/10.1016/j.future.2025.108314. https://hdl.handle.net/10630/45359 https://doi.org/10.1016/j.future.2025.108314 Seguridad informática Internet de los objetos Energía eléctrica - Distribución Ciberterrorismo Dispatching advanced and adaptive intrusion responses for IIoT-based system