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This paper proposes REACT-D3QN (Resilient Adaptive Concept-drift-aware Dueling Double Deep Q-Network), a framework designed to maintain operationally relevant detection integrity during cross-dataset migration. By integrating a Dueling architecture, the framework decouples state-value estimation from action advantages, allowing the agent to recognize the \"intrinsic risk\" of network states even under extreme feature pruning. A core innovation of this work is the REACT reward engine, which utilizes a 10:1 asymmetric penalty ratio to establish a \"Recall Floor\", prioritizing the detection of drifted attack signatures over simple accuracy. Evaluated through a zero-base transfer learning transition from CIC-IDS2017 to CIC-IDS2018 traffic patterns, REACT-D3QN achieved a 99.85% Recall while operating on an 84.4% reduced feature set (5 features). These results prove that architectural resilience can compensate for significant signal loss, providing a robust pathway for deploying high-performance Intrusion Detection Systems (IDS) on low-power edge infrastructure and setting the stage for decentralized, blockchain-verified autonomous defense.<\/jats:p>","DOI":"10.1186\/s42400-026-00558-7","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T23:18:58Z","timestamp":1772320738000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["React-d3qn: resilient adaptive concept-drift-aware dueling double deep Q-network for robust edge-centric intrusion detection"],"prefix":"10.1186","volume":"9","author":[{"given":"Ibtissam","family":"Haddane","sequence":"first","affiliation":[]},{"given":"Badr","family":"Hirchoua","sequence":"additional","affiliation":[]},{"given":"Hicham","family":"Moutachaouik","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"issue":"3","key":"558_CR1","first-page":"132","volume":"2","author":"AA Abdulrahman","year":"2020","unstructured":"Abdulrahman AA, Ibrahem MK (2020) Toward constructing a balanced intrusion detection dataset based on CICIDS2017. 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