{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T20:31:33Z","timestamp":1758054693490,"version":"3.44.0"},"reference-count":39,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"DOI":"10.13039\/501100023674","name":"Deanship of Scientific Research, King Khalid University","doi-asserted-by":"publisher","award":["RSPD2023R537"],"award-info":[{"award-number":["RSPD2023R537"]}],"id":[{"id":"10.13039\/501100023674","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Coop. Info. Syst."],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p> The prevalence of distributed denial of service (DDoS) flooding assaults is one of the most serious risks to cloud computing security. These types of assaults have as their primary objective the exhaustion of the system\u2019s available resources, that is, the target of the attack, in order to make the system in question unavailable to authorized users. Internet thieves often conduct flooding assaults of the kind known as DDoS, focusing primarily on the application and network levels. When the computer infrastructure is multi-mesh-geo distributed, includes multi-parallel services, and a high number of domains, it may be difficult to detect assaults. This is particularly true when a substantial number of domains are present. When there are a big number of independent administrative users using the services, the situation gets more complicated. The purpose of this body of research is to identify signs that may be utilized to detect DDoS flooding assaults; this is its main objective. As a result, throughout the course of our study, we established a composite metric that considers application, system, network, and infrastructure elements as possible indicators of the incidence of DDoS assaults. According to our research, DDoS assaults may be triggered by a combination of variables. Investigations of simulated traffic are being conducted in the cloud. High traffic may be the result of flooding assaults. The composite metric-based intrusion detection system will be the name of a one-of-a-kind intrusion detection system (IDS) that has been agreed upon ICMIDS. This system will use [Formula: see text]-Means clustering and the Genetic Algorithm (GA) to detect whether an effort has been made to flood the cloud environment. CMIDS employs a multi-threshold algorithmic strategy in order to identify malicious traffic occurring on a cloud-based network. Cisco has created this technology. This strategy necessitates a comprehensive investigation of all factors, which is crucial for assuring the continuation of cloud-based computing-based activities. This monitoring system involves the development, administration, and storage of a profile database, denoted as Profile DB. This database is used for recording and using the composite metric for each virtual machine. The results of a series of tests are compared to the ISCX benchmark dataset and statistical settings. The results indicate that ICMIDS has a reasonably high detection rate and the lowest false alarm rate in the majority of situations examined during the series of tests done to validate and verify its efficacy. This was shown by the fact that ICMIDS had the lowest false alarm rate among all examined conditions. <\/jats:p>","DOI":"10.1142\/s0218843024500035","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T09:18:06Z","timestamp":1705569486000},"source":"Crossref","is-referenced-by-count":0,"title":["AI-Assisted Enhanced Composite Metric-Based Intrusion Detection System for Secured Cyber Internet Security for Next-Generation Wireless Networks"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2307-5980","authenticated-orcid":false,"given":"Amani K.","family":"Samha","sequence":"first","affiliation":[{"name":"Management Information System Department, College of Business Administration, King Saud University Riyadh 28095, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8317-6498","authenticated-orcid":false,"given":"Ghalib H.","family":"Alshammri","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Community College, King Saud University, Riyadh 11437, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5710-1864","authenticated-orcid":false,"given":"Sasidhar","family":"Attuluri","sequence":"additional","affiliation":[{"name":"Savin Technologies Inc., 9901 Valley Ranch Pkwy E, Irving, TX, 75063, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7607-1372","authenticated-orcid":false,"given":"Preetam","family":"Suman","sequence":"additional","affiliation":[{"name":"CSE-Core, School of Computing Science and Engineering, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, Sehore, Madhya Pradesh \u2013 466114, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8988-9947","authenticated-orcid":false,"given":"Arvind","family":"Yadav","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, SRM University, Delhi-NCR Sonepat 131029, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2024,3,18]]},"reference":[{"key":"S0218843024500035BIB001","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"S0218843024500035BIB002","doi-asserted-by":"publisher","DOI":"10.1126\/science.1127647"},{"key":"S0218843024500035BIB003","first-page":"306","volume-title":"Intelligent Signal Processing","author":"LeCun Y.","year":"2001"},{"key":"S0218843024500035BIB004","doi-asserted-by":"publisher","DOI":"10.1113\/jphysiol.1962.sp006837"},{"volume-title":"IEEE Int. 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