{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:55:00Z","timestamp":1782233700362,"version":"3.54.5"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>Intelligent transport systems are increasingly dependent on\ninterconnected devices and vehicular communications, making\nthem vulnerable to reconnaissance attacks that precede\nlarge-scale intrusions. Traditional intrusion detection\napproaches often struggle with the scalability, redundancy\nof features, and complexity of dynamic traffic\nenvironments. To address these challenges, this paper\nintroduces an LSH-Based Sparse Attention LLM Framework for\nreconnaissance attack detection. The framework applies\nSaint-Bowerbird Optimization (SBO) to select 23 optimal\nfeatures from 41, ensuring efficiency and reducing\nredundancy. Categorical embeddings with drift analysis\nvalidate meaningful representation learning, while\nLSH-based sparse attention focuses on critical feature\ninteractions with reduced complexity. The experimental\nresults show that the model achieves 99.99% precision,\noutperforming RNN, LSTM, GRU and state-of-the-art models,\nconfirming its robustness to secure intelligent transport\nsystems.<\/jats:p>","DOI":"10.1609\/aaaiss.v9i1.42901","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:33:24Z","timestamp":1782232404000},"page":"19-26","source":"Crossref","is-referenced-by-count":0,"title":["Large Language Model (LLM) Based Resilient Intelligent Transport Systems"],"prefix":"10.1609","volume":"9","author":[{"given":"Brij","family":"B. Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akshat","family":"Gaurav","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valerie","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Varsha","family":"Arya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amiya","family":"Nayak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kwok","family":"Tai Chui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9382","published-online":{"date-parts":[[2026,6,23]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/42901\/50461","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/42901\/50461","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:33:24Z","timestamp":1782232404000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/42901"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,6,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v9i1.42901","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,23]]}}}