{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:48:51Z","timestamp":1777704531886,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2020,4,6]],"date-time":"2020-04-06T00:00:00Z","timestamp":1586131200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,5,29]]},"abstract":"<jats:p>\n                    \u00a0This paper attempts to employ Evolutionary Algorithm(EA) techniques to evolve variants of a computer virus(\n                    <jats:italic>Timid<\/jats:italic>\n                    ) that successfully evades popular antivirus scanners. Generating authentic variants of a specific malware results in a valid database of malware variants, which is sought by anti-malware scanners, so as to identify the variants before they are released by malware developers. This preliminary investigation applies EAs to mutate the\n                    <jats:italic>Timid<\/jats:italic>\n                    virus with a simple code evasion strategy, i.e., insertion and deletion(if available) of a specific assembly code instruction directly into the virus source code. Starting with a database of over 60 popular antivirus scanners, this EA based approach for malware variant generation successfully evolves\n                    <jats:italic>Timid<\/jats:italic>\n                    variants that evade more than 97% of the antivirus scanners. The results from these preliminary investigations demonstrate the potential for EA based malware generation and also opens up avenues for further analysis.\n                  <\/jats:p>","DOI":"10.3233\/jifs-179732","type":"journal-article","created":{"date-parts":[[2020,4,7]],"date-time":"2020-04-07T13:57:40Z","timestamp":1586267860000},"page":"6517-6526","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["A preliminary investigation into automatically evolving computer viruses using evolutionary algorithms"],"prefix":"10.1177","volume":"38","author":[{"given":"Ritwik","family":"Murali","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Engineering - Coimbatore, Amrita Vishwa Vidyapeetham, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C.","family":"Shunmuga Velayutham","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Engineering - Coimbatore, Amrita Vishwa Vidyapeetham, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2020,4,6]]},"reference":[{"issue":"4","key":"e_1_3_1_2_2","first-page":"31","article-title":"Artificial immune clonal selection classification algorithms for classifying malware and benign processes using api call sequences","volume":"10","author":"Al-Sheshtawi K.A.","year":"2010","unstructured":"Al-SheshtawiK.A., Abdul-KaderH. and IsmailN.A., Artificial immune clonal selection classification algorithms for classifying malware and benign processes using api call sequences, International Journal of Computer Science and Network Security10(4) (2010), 31\u201339.","journal-title":"International Journal of Computer Science and Network Security"},{"key":"e_1_3_1_3_2","doi-asserted-by":"crossref","unstructured":"AydoganE. and SenS. 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