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The proposed approach \u201cAC_MAPPER\u201d consistently outperforms all baselines in both Accuracy and F1 scores across five benchmark datasets, achieving up to\n                    <jats:bold>93.59%<\/jats:bold>\n                    accuracy and\n                    <jats:bold>93.78%<\/jats:bold>\n                    macro F1 on the TRAM Bootstrap dataset. It also demonstrates superior robustness on highly imbalanced and sparse datasets such as HALdata and CAPEC, where baseline models struggle. Comprehensive performance comparisons, highlights the effectiveness of proposed approach. These results underscore the potential of integrating domain-specific design with transformer architectures to advance automated CTI analysis. Our findings contribute toward more accurate and reliable threat detection systems in real-world security applications.\n                  <\/jats:p>","DOI":"10.1007\/s10207-025-01146-5","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T14:39:34Z","timestamp":1762526374000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AC_MAPPER: a robust approach to ATT&amp;CK technique classification using input augmentation and class rebalancing"],"prefix":"10.1007","volume":"24","author":[{"given":"Majed","family":"Albarrak","sequence":"first","affiliation":[]},{"given":"Adel","family":"Alqudhaibi","sequence":"additional","affiliation":[]},{"given":"Sandeep","family":"Jagtap","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,11,7]]},"reference":[{"key":"1146_CR1","doi-asserted-by":"crossref","unstructured":"Institute IBMS-P (2023) Cost of a data breach report 2022","DOI":"10.12968\/S1353-4858(22)70049-9"},{"key":"1146_CR2","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1007\/s11227-025-07255-1","volume":"81","author":"A Nazir","year":"2025","unstructured":"Nazir, A., He, J., Zhu, N., et al.: Empirical evaluation of ensemble learning and hybrid CNN-LSTM for IoT threat detection on heterogeneous datasets. 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