{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:52:14Z","timestamp":1778172734313,"version":"3.51.4"},"reference-count":48,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T00:00:00Z","timestamp":1748563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>With the growing prevalence of cybercrime, botnets have emerged as a significant threat, infiltrating an increasing number of legitimate computers annually. Challenges arising for organizations, educational institutions, and individuals as a result of botnet attacks include distributed denial of service (DDoS) attacks, phishing attacks, and extortion attacks, generation of spam, and identity theft. The stealthy nature of botnets, characterized by constant alterations in network structures, attack methodologies, and data transmission patterns, poses a growing difficulty in their detection. This paper introduces an innovative strategy for mitigating botnet threats. Employing differential evolution, we propose a feature selection approach that enhances the ability to discern peer-to-peer (P2P) botnet traffic amidst evolving cyber threats. Differential evolution is a population-based meta-heuristic technique which can be applied to nonlinear and non-differentiable optimization problems owing to its fast convergence and use of few control parameters. Apart from that, an ensemble learning algorithm is also employed to support and enhance the detection phase, providing a robust defense against the dynamic and sophisticated nature of modern P2P botnets. The results demonstrate that our model achieves 99.99% accuracy, 99.49% precision, 98.98% recall, and 99.23% F1-score, which outperform the state-of-the-art P2P detection approaches.<\/jats:p>","DOI":"10.3390\/fi17060247","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T08:34:13Z","timestamp":1748853253000},"page":"247","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection"],"prefix":"10.3390","volume":"17","author":[{"given":"Sangita","family":"Baruah","sequence":"first","affiliation":[{"name":"Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA"},{"name":"Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6361-442X","authenticated-orcid":false,"given":"Vaskar","family":"Deka","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Gauhati University, Guwahati 781014, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9211-2314","authenticated-orcid":false,"given":"Dulumani","family":"Das","sequence":"additional","affiliation":[{"name":"Faculty of Computer Technology, Assam Down Town University, Guwahati 781026, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Utpal","family":"Barman","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Assam Skill University, Mangaldoi 784125, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6656-4333","authenticated-orcid":false,"given":"Manob Jyoti","family":"Saikia","sequence":"additional","affiliation":[{"name":"Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA"},{"name":"Electrical and Computer Engineering Department, University of Memphis, Memphis, TN 38152, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.21015\/vtcs.v9i1.604","article-title":"A survey of feature extraction and feature selection techniques used in machine learning-based botnet detection schemes","volume":"9","author":"Oyelakin","year":"2021","journal-title":"VAWKUM Trans. 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