AI-Powered Botnet Detection Systems: A Critical Review of Current Approaches and Challenges

Title

AI-Powered Botnet Detection Systems: A Critical Review of Current Approaches and Challenges

Creator

Vinodha, D.; Mary Anita, E.A.; Lapina, Maria

Description

In the era of information technology, Botnets have become the most persistent cyber threat, capable of launching large-scale attacks like Distributed Denial of Service. stealing sensitive information and disturbing online services. Botnets have evolved from simple networks to complicated distributed networks including IoT devices, making them pervasive, harder to track, and destroy. Machine learning and Deep learning based models are emerging to detect bot attacks by analyzing large datasets and detecting patterns and anomalies. The state of the art methodologies for detecting bot infection are reviewed deeply and compared based on adopted methodologies, dataset and feature selection mechanism. The paper further discusses the pros and cons of existing methodologies. Finally, research gaps are presented to help future research on enhancing bot detection. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Source

Lecture Notes in Networks and Systems;Volume;1456 LNNS;pp.93-104

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Botnet; Deep Learning; Internet of Things; Machine learning; network traffic analysis

Coverage

Vinodha D., Christ University, Bangalore, India; Mary Anita E.A., Christ University, Bangalore, India; Lapina M., North Caucasus Federal University, Stavropol, Russian Federation

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-303207274-0;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-032-07275-7_10

https://www.scopus.com/pages/publications/105023167339?origin=resultslist