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Kapil Kumar, Manju Khari,
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- Research Scholar, Professor School of Computer and Systems Sciences, Jawaharlal Nehru University, School of Computer and Systems Sciences, Jawaharlal Nehru University New Delhi, New Delhi India, India
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Abstract
nSecuring information technology systems is paramount in today’s interconnected world, where the reliability and security of networks and applications are of utmost importance. In this context, the development of a Botnet Detection System (BDS) that harnesses the power of AI classification algorithms becomes a critical endeavor. The primary objective of this work is to construct a comprehensive framework for a BDS that can efficiently gather network data and subject it to rigorous analysis using AI algorithms. To achieve this objective, the authors have chosen to utilize the botnet dataset, a widely recognized benchmark in the field, for training the classifier. This dataset serves as a rich source of information containing various network traffic data, particularly focusing on essential features that are crucial for attack classification. By employing this dataset, the authors ensure that the BDS is trained on a diverse set of network behaviors and attack patterns, enabling it to recognize and differentiate between legitimate and malicious activities effectively. The performance of the BDS is evaluated through a rigorous assessment, encompassing metrics such as accuracy, precision, and detection rate. These metrics are essential in gauging the BDS’s ability to correctly identify and classify botnet activities while minimizing false positives. By conducting this comprehensive evaluation, the authors aim to ensure that the BDS is not only capable of detecting botnets but does so with a high degree of accuracy and reliability.
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Keywords: Botnet Detection System (BDS), Artificial Neural Network (ANN), software-defined networking (SDN), machine learning, information security
n[if 424 equals=”Regular Issue”][This article belongs to Research & Reviews: A Journal of Embedded System & Applications(rrjoesa)]
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Research & Reviews: A Journal of Embedded System & Applications
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| Volume | 12 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | July 17, 2024 | |
| Accepted | July 23, 2024 | |
| Published | July 26, 2024 |
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