{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T14:32:44Z","timestamp":1779114764840,"version":"3.51.4"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>To address the problems of high-dimensional feature redundancy, weak generalization ability of small- sample attacks, and insufficient detection-response coordination in network intrusion detection, this study proposes an end-to-end intelligent detection and proactive response framework based on Feature Adaptive Distillation-Attention Enhanced Siamese Network (FAD-AESN). We adopt a three-stage methodological approach: (1) a Feature Adaptive Distillation (FAD) module for adaptive dimensionality reduction, which integrates feature discriminativeness, correlation and attack relevance to dynamically adjust distillation temperature and feature weights, realizing redundant feature removal and core information preservation; (2) an Attention-Enhanced Siamese Network (AESN) module with an embedded channel-space attention mechanism and improved triplet loss function to enhance the differentiated expression of small-sample attack features; (3) a dynamic proactive response mechanism constructed based on detection confidence and multidimensional threat-level assessment, forming a closed-loop detection-response coordination system. We conduct comprehensive experiments on the CSE-CIC-IDS2018 and UNSW-NB15 datasets with 5 independent experimental runs for each algorithm; the full-sample dataset is split into training and test sets at a 7:3 ratio, and the small-sample dataset is split into support and query sets at a 6:4 ratio, with all results reported as the mean \u00b1 standard deviation. Statistical significance is verified via two-tailed t-tests (p<\/jats:p>","DOI":"10.31449\/inf.v50i13.13215","type":"journal-article","created":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T13:54:34Z","timestamp":1779112474000},"source":"Crossref","is-referenced-by-count":0,"title":["Feature Adaptive Distillation and Attention-Enhanced Siamese Network for Intelligent Network Intrusion Detection and Proactive Response"],"prefix":"10.31449","volume":"50","author":[{"given":"Yong","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fan","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,5,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/13215\/6718","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/13215\/6718","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T13:54:35Z","timestamp":1779112475000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/13215"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,18]]},"references-count":0,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2026,5,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i13.13215","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,18]]}}}