{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T07:46:23Z","timestamp":1763451983475,"version":"3.45.0"},"reference-count":38,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T00:00:00Z","timestamp":1758585600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Security and Privacy"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>The 5G network is a new communication network development that mainly involves network slicing (NS) into isolated virtual networks dedicated to applications, industries, or user groups. Despite the flexibility provided by NS, it faces security challenges, especially denial\u2010of\u2010service (DoS)\/distributed DoS attacks among different network slices on the 5G network. The proposed methodology in this study involves two levels of detection: at the slice level and at the cross level. Joint entropy with a dynamic threshold is used for early detection at the slice level, and a developed deep neural network (DDNN) model that uses a new activation function called the sigmoid\u2013exponential\u2013threshold activation function (SETAF) is employed for deep detection at the cross level. The CICIDS2017 dataset and NSDS are employed to build and evaluate the DDNN model. Results from the NS testbed show that the model effectively detects various attack patterns, achieving a detection accuracy of 99% with an average detection latency of approximately 49 ms. In addition, the results showed that using the new activation function led to a significant increase in accuracy, with most of the accuracy criteria reaching 0.994, and it also contributed to reducing training time to 33.1226\u2009s and reducing mean square error to 0.0037. Thus, the SETAF function contributes to improving the detection of attacks in a dynamic manner that adapts to changes in network traffic.<\/jats:p>","DOI":"10.1002\/spy2.70108","type":"journal-article","created":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T06:07:14Z","timestamp":1758694034000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Proposing a Cross\u2010Level Deep Neural Denial of Service\/Distributed Denial of Service Attacks Detection in\n                    <scp>5G<\/scp>\n                    Network Slicing"],"prefix":"10.1002","volume":"8","author":[{"given":"Suadad S.","family":"Mahdi","sequence":"first","affiliation":[{"name":"College of Information Technology University of Babylon  Babil Iraq"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1485-2461","authenticated-orcid":false,"given":"Alharith A.","family":"Abdullah","sequence":"additional","affiliation":[{"name":"College of Information Technology University of Babylon  Babil Iraq"}]}],"member":"311","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"volume-title":"5G Mobile Communications","year":"2016","author":"Xiang W.","key":"e_1_2_9_2_1"},{"issue":"6","key":"e_1_2_9_3_1","first-page":"2278","article-title":"A Comparative Study on 4G and 5G Technology for Wireless Applications","volume":"10","author":"Gopal B. G.","year":"2015","journal-title":"IOSR Journal of Electronics and Communication Engineering"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-16170-5"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.121.2100172"},{"key":"e_1_2_9_6_1","first-page":"733","volume-title":"International Conference on Information Systems and Intelligent Applications","author":"Mahdi S. S.","year":"2022"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2018.1800045"},{"key":"e_1_2_9_8_1","first-page":"10","volume-title":"2022 Muthanna International Conference on Engineering Science and Technology (MICEST)","author":"Sattar S.","year":"2022"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11235-020-00747-w"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2023.3312349"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.4018\/IJSWIS.297143"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.4018\/IJSSCI.309707"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3090909"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CNS.2019.8802852"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2800"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCWC47524.2020.9031158"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/LWC.2021.3133479"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM48099.2022.10001562"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2022.3193074"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/FNWF55208.2022.00117"},{"key":"e_1_2_9_21_1","doi-asserted-by":"crossref","unstructured":"S.Hossain A.Boualouache B.Brik andS.\u2010M.Senouci \u201cA Lightweight 5G\u2010V2X Intra\u2010Slice Intrusion Detection System Using Knowledge Distillation \u201d(2023).","DOI":"10.1109\/ICC45041.2023.10279212"},{"key":"e_1_2_9_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCNC51644.2023.10060402"},{"key":"e_1_2_9_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNSM.2023.3294568"},{"key":"e_1_2_9_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2024.107927"},{"key":"e_1_2_9_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2025.104826"},{"key":"e_1_2_9_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/CISIS.2010.53"},{"key":"e_1_2_9_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apnum.2021.04.013"},{"key":"e_1_2_9_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/e23081046"},{"key":"e_1_2_9_29_1","doi-asserted-by":"publisher","DOI":"10.1201\/9780203749340"},{"key":"e_1_2_9_30_1","doi-asserted-by":"publisher","DOI":"10.11591\/eei.v11i3.3688"},{"key":"e_1_2_9_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2020.107792"},{"key":"e_1_2_9_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3009843"},{"issue":"12","key":"e_1_2_9_33_1","first-page":"310","article-title":"Activation Functions in Neural Networks","volume":"6","author":"Sharma S.","year":"2017","journal-title":"Towards Data Science"},{"key":"e_1_2_9_34_1","first-page":"173","volume-title":"International Conference on New Trends in Information and Communications Technology Applications","author":"Mahdi S. S.","year":"2022"},{"key":"e_1_2_9_35_1","first-page":"132","article-title":"Flowvisor: A Network Virtualization Layer","volume":"1","author":"Sherwood R.","year":"2009","journal-title":"OpenFlow Switch Consortium, Technical Report"},{"key":"e_1_2_9_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2018.2841349"},{"key":"e_1_2_9_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2022.109301"},{"key":"e_1_2_9_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-4470-8_18"},{"key":"e_1_2_9_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2976609"}],"container-title":["SECURITY AND PRIVACY"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/spy2.70108","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T07:43:14Z","timestamp":1763451794000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/spy2.70108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,23]]},"references-count":38,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11]]}},"alternative-id":["10.1002\/spy2.70108"],"URL":"https:\/\/doi.org\/10.1002\/spy2.70108","archive":["Portico"],"relation":{},"ISSN":["2475-6725","2475-6725"],"issn-type":[{"type":"print","value":"2475-6725"},{"type":"electronic","value":"2475-6725"}],"subject":[],"published":{"date-parts":[[2025,9,23]]},"assertion":[{"value":"2024-06-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70108"}}