{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:12:24Z","timestamp":1778602344478,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN\u2013GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.<\/jats:p>","DOI":"10.3390\/s23020890","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T04:29:38Z","timestamp":1673497778000},"page":"890","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":101,"title":["Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System"],"prefix":"10.3390","volume":"23","author":[{"given":"Azriel","family":"Henry","sequence":"first","affiliation":[{"name":"Department of Computer Sciences and Engineering, Institute of Advanced Research, Gandhinagar 382426, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sunil","family":"Gautam","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samrat","family":"Khanna","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences and Engineering, Institute of Advanced Research, Gandhinagar 382426, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9784-3703","authenticated-orcid":false,"given":"Khaled","family":"Rabie","sequence":"additional","affiliation":[{"name":"Department of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK"},{"name":"Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Auckland Park, P.O. Box 524, Johannesburg 2006, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thokozani","family":"Shongwe","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering Technology, University of Johannesburg, Auckland Park, P.O. Box 524, Johannesburg 2006, South Africa"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1206-2298","authenticated-orcid":false,"given":"Pronaya","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Kolkata, 700135, West Bengal, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3400-3504","authenticated-orcid":false,"given":"Bhisham","family":"Sharma","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Subrata","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Masters of Computer Application, Sri Venkateswara College of Engineering and Technology (A), Chittoor 517127, Andhra Pradesh, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105980","DOI":"10.1016\/j.asoc.2019.105980","article-title":"An efficient feature selection based Bayesian and Rough set approach for intrusion detection","volume":"87","author":"Prasad","year":"2020","journal-title":"Appl. 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