ML-enabled intelligent Bot detection in network communications

Manasu Madhavi; M. Soumya; M. Vani; M. Pavani

Author(s)

Manasu Madhavi Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana,India

M. Soumya Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana,India

M. Vani Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana,India

M. Pavani Department of Information Technology Malla Reddy Engineering College for Women (UGC-Autonomous), Maisammaguda, Hyderabad , Telangana,India

Abstract

As the Internet of Things (IoT) continues to proliferate across diverse sectors, the security of IoT networks has become increasingly critical. Distributed Denial of Service (DDoS) BOT attacks pose a significant threat by overwhelming networks with malicious traffic, resulting in service disruptions, data breaches, and financial losses. Traditional detection systems rely heavily on signature-based approaches, which are ill-suited for zero-day attacks and lack adaptability to evolving threat landscapes. Additionally, these legacy solutions often focus on the network perimeter, offering limited insights into the heterogeneous and granular nature of IoT environments. In response, this research introduces DDOSNET, a novel DDoS detection and classification framework designed specifically for IoT ecosystems. Leveraging machine learning algorithms and advanced data analytics, DDOSNET performs real-time monitoring of network traffic to identify anomalous patterns indicative of DDoS activity. In contrast to existing methods, DDOSNET considers the unique features of IoT environments, including multiple communication protocols and diverse device profiles, enabling accurate detection at the device level. The system incorporates a robust pipeline-encompassing feature extraction, data preprocessing, and model training-to address both known and emerging DDoS vectors. By integrating DDOSNET, organizations can proactively mitigate DDoS threats, ensuring uninterrupted operations and bolstering the overall security posture of IoT deployments.

Publication Details

Page(s) 681-690

DOI DOI not available

Published Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 05, Year: 2024

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Keywords

Internet of Things, Distributed Denial of Service, Network Traffic, IoT Ecosystems, DDOSNET

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