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With rapid advancements in computing and digital technologies, cybersecurity threats have also become more complex and widespread. Among these, botnets represent a significant challenge that demands continuous research and innovative solutions. This study investigates the detection of botnet threats by applying machine learning models to well-known cybersecurity datasets, including Bot-IoT and UNSW-NB15. We explore and compare the effectiveness of classification algorithms such as Naïve Bayes, KNN, SVM, and Decision Tree. The Decision Tree model demonstrated the highest performance, achieving a testing accuracy of 99.89%, with perfect precision, recall, and F1-score, using a subset of 82,000 records from the UNSW-NB15 dataset.
Keywords:
BOTNET, MACHINE LEARNING, CYBERATTACK, DDOS
Cite Article:
"Botnet Attack Detection using Machine Learning", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b531-b534, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504164.pdf
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000375
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator