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Deep learning models have gained popularity for extracting high-level feature representations from massive datasets. In this work, a novel deep neural network architecture, supervised learning based LD-BiHGA (Low Dimensional Bi-channel Hybrid GAN Attention) system is designed to learn class-specific features for accurate anomaly detection. Two asymmetric GANs are employed for learning the normal and abnormal network flows separately. Then, to extract more relevant features, a bi-channel attention mechanism is added. This is the first study to introduce an innovative hybrid architecture that merges bi-channel hybrid GANs with attention models for the purpose of anomaly detection in a multi-domain SDN environment that effectively handles real-time unbalanced data. The suggested architecture demonstrates its effectiveness on three benchmark datasets, achieving an average accuracy improvement of 7.225% on balanced datasets and 3.335% on imbalanced datasets compared to previous intrusion detection system (IDS) architectures in the literature.<\/jats:p>","DOI":"10.3233\/jifs-233668","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T12:13:13Z","timestamp":1699013593000},"page":"457-478","source":"Crossref","is-referenced-by-count":1,"title":["Bi-channel hybrid GAN attention based anomaly detection system for multi-domain SDN environment"],"prefix":"10.1177","volume":"46","author":[{"given":"Saranya","family":"Prabu","sequence":"first","affiliation":[{"name":"Department of Computer Technology, MIT Campus, Anna University, Chennai, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jayashree","family":"Padmanabhan","sequence":"additional","affiliation":[{"name":"Department of Computer 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