{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:13:01Z","timestamp":1753884781368,"version":"3.41.2"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:p> The K-variant is a multi-variant architecture to enhance the security of\u200athe time-bounded mission and safety-critical systems. Variants in the K-variant architecture are generated by controlled source program transformations. Previous experimental studies showed that the K-variant architecture might improve the security of systems against memory exploitation attacks. In order to estimate the survivability of K-variant systems, simulation techniques are utilized. However, these techniques are slow and may not be practical for the design of K-variant systems. Therefore, fast and highly accurate estimations of the survivability of K-variant systems are necessary for developers. The neural networks may allow quick and accurate estimation of the survivability of K-variant systems. The developed neural network-based tool can make quick and precise estimations of the survivability of K-variant systems under different conditions. In this paper, the accuracy of the neural network-based tool is investigated in an experimental study. The neural network-based tool estimations are compared with a K-variant attack emulator in three programs for up to ten variant systems under four attack types and three attack durations. The experimental study demonstrates that the neural network-based tool makes fast and accurate estimations of the survivability of K-variant systems under all the conditions investigated. <\/jats:p>","DOI":"10.1142\/s0218213023500495","type":"journal-article","created":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T02:43:42Z","timestamp":1684896222000},"source":"Crossref","is-referenced-by-count":1,"title":["Neural Network-based Tool for Survivability Assessment of K-variant Systems"],"prefix":"10.1142","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9429-0990","authenticated-orcid":false,"given":"Berk","family":"Bekiroglu","sequence":"first","affiliation":[{"name":"Department of Computer Science, Illinois Institute of Technology, 10 West 31st Street, Chicago, IL 60616, USA"}]},{"given":"Bogdan","family":"Korel","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Illinois Institute of Technology, 10 West 31st Street, Chicago, IL 60616, USA"}]}],"member":"219","published-online":{"date-parts":[[2023,5,24]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213023500495","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T00:41:17Z","timestamp":1688085677000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218213023500495"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,24]]},"references-count":0,"journal-issue":{"issue":"04","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["10.1142\/S0218213023500495"],"URL":"https:\/\/doi.org\/10.1142\/s0218213023500495","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"type":"print","value":"0218-2130"},{"type":"electronic","value":"1793-6349"}],"subject":[],"published":{"date-parts":[[2023,5,24]]},"article-number":"2350049"}}