{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T20:41:19Z","timestamp":1757450479007,"version":"3.40.3"},"reference-count":0,"publisher":"SASA Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JoWUA"],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>A cyber-attack is the malicious manipulation of computer networks and systems to compromise data \nor impede procedures and operations using malware. With the exponential growth in computational \ncapacity, machine learning (ML) and deep learning (DL) approaches have emerged as promising \ncountermeasures for advancing and identifying such threats. To address this challenge, a novel \noptimized deep hybrid attack detection model called SCEEHO-SPC-CNN-CD-DBN is proposed in \nthis research article. Data is subjected to a preprocessing procedure before it is used for further \nprocesses. Here, the data undergoes a normalizing phase for pre-processing, during which the \nstatistics and higher-order statistical features are retrieved. The cyber-attack detection process \nconcludes with a hybrid DL model applied to the retrieved features. The proposed hybrid classifier \nintegrates models such as the DBN (Deep Belief Network) with contrastive divergence (CD) and \nthe split convolution module (SPC)-based CNN (Convolutional Neural Network). Training the CNN \nand DBN using the SCEEHO(Sea CrowEndorsed Elephant Herding optimization) model and fine\ntuning the ideal weights improves detection accuracy. Furthermore, have tested the \ndevelopedSCEEHO-SPC-CNN-CD-DBN-based hybrid classifier on the CIC IoT Dataset 2023. The \nevaluated results, employing a wide range of statistical measures, demonstrate that the research \nmodel performs efficiently.<\/jats:p>","DOI":"10.58346\/jowua.2025.i1.003","type":"journal-article","created":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T11:51:18Z","timestamp":1743681078000},"page":"49-71","source":"Crossref","is-referenced-by-count":1,"title":["Cyber Attack Recognition in an Internet of Things-Enabled  Environment Using a Hybrid Optimised Deep Learning  Approach"],"prefix":"10.58346","volume":"16","author":[{"given":"Boyella Mala Konda","family":"Reddy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dr.A.","family":"Abdul Azeez Khan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dr.K.","family":"Javubar Sathick","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dr.L.","family":"Arun Raj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"37075","published-online":{"date-parts":[[2025,3,31]]},"container-title":["Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications"],"original-title":[],"deposited":{"date-parts":[[2025,4,3]],"date-time":"2025-04-03T11:51:21Z","timestamp":1743681081000},"score":1,"resource":{"primary":{"URL":"https:\/\/jowua.com\/wp-content\/uploads\/2025\/04\/2025.I1.003.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,31]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,3,31]]},"published-print":{"date-parts":[[2025,3,31]]}},"URL":"https:\/\/doi.org\/10.58346\/jowua.2025.i1.003","relation":{},"ISSN":["2093-5374"],"issn-type":[{"value":"2093-5374","type":"print"}],"subject":[],"published":{"date-parts":[[2025,3,31]]}}}