Botnet Beacon: Unveiling Covert Networks with Advanced AI Detection Strategies

Year : 2024 | Volume :12 | Issue : 02 | Page : –
By

Kapil Kumar,

Manju Khari,

  1. Research Scholar School of Computer and Systems Sciences, Jawaharlal Nehru University New Delhi India
  2. Professor School of Computer and Systems Sciences, Jawaharlal Nehru University New Delhi India

Abstract

Securing 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.

Keywords: Botnet Detection System (BDS), Artificial Neural Network (ANN), software-defined networking (SDN), machine learning, information security

[This article belongs to Research & Reviews: A Journal of Embedded System & Applications(rrjoesa)]

How to cite this article: Kapil Kumar, Manju Khari. Botnet Beacon: Unveiling Covert Networks with Advanced AI Detection Strategies. Research & Reviews: A Journal of Embedded System & Applications. 2024; 12(02):-.
How to cite this URL: Kapil Kumar, Manju Khari. Botnet Beacon: Unveiling Covert Networks with Advanced AI Detection Strategies. Research & Reviews: A Journal of Embedded System & Applications. 2024; 12(02):-. Available from: https://journals.stmjournals.com/rrjoesa/article=2024/view=158270

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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received July 17, 2024
Accepted July 23, 2024
Published July 26, 2024
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