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Research Article

Knox: Lightweight Machine Learning Approaches for Automated Detection of Botnet Attacks

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  • @ARTICLE{10.4108/eetsis.3997,
        author={Shritik Raj and Bernard Ngangbam and Sanket Mishra and Vivek Gopalasetti and Ayushi Bajpai and Ch. Venkata Rami Reddy},
        title={Knox: Lightweight Machine Learning Approaches for Automated Detection of Botnet Attacks},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={11},
        number={1},
        publisher={EAI},
        journal_a={SIS},
        year={2023},
        month={9},
        keywords={Machine Learning, Botnet Detection, Internet of Things, Dimensionality Reduction, Data Sampling Techniques, Data streaming, Feature Extraction},
        doi={10.4108/eetsis.3997}
    }
    
  • Shritik Raj
    Bernard Ngangbam
    Sanket Mishra
    Vivek Gopalasetti
    Ayushi Bajpai
    Ch. Venkata Rami Reddy
    Year: 2023
    Knox: Lightweight Machine Learning Approaches for Automated Detection of Botnet Attacks
    SIS
    EAI
    DOI: 10.4108/eetsis.3997
Shritik Raj1,*, Bernard Ngangbam1, Sanket Mishra1, Vivek Gopalasetti1, Ayushi Bajpai1, Ch. Venkata Rami Reddy1
  • 1: Vellore Institute of Technology University
*Contact email: rajshritik03@gmail.com

Abstract

With an advancement in technology, the Internet of Things (IoT) has penetrated various domains such as smart buildings, intelligent transportation systems, healthcare, smart parking, air quality monitoring, water contamination identification, and supply chain owing to its ubiquitous nature. IoT devices periodically collect the data and send it to the gateway or server for pre-processing. However, the security offered in the IoT devices or gateways are still in a nascent stage. An Intrusion Detection System (IDS) meant for detecting the cyber threats on IoT should intercept most threats with minimum latency and yet be lightweight in nature. IoT devices also have low memory footprint which makes them resource constrained. This paper presents a framework built using a three-tier IoT architecture that successfully detects most attacks using machine learning approaches with an accuracy of 99%. Machine learning approaches are fed data using Apache Kafka to REST API. Sampling methods such as undersampling and adaptive synthetic sampling are applied to balance the imbalanced nature of the dataset. We examined the robustness of the approach using different samples with varying sizes and varying dimensions. Experimental results depict a superior performance of random forest over other approaches in terms of speed and accuracy.

Keywords
Machine Learning, Botnet Detection, Internet of Things, Dimensionality Reduction, Data Sampling Techniques, Data streaming, Feature Extraction
Received
2023-06-14
Accepted
2023-08-26
Published
2023-09-26
Publisher
EAI
http://dx.doi.org/10.4108/eetsis.3997

Copyright © 2023 S. Raj et al., licensed to EAI. This is an open access article distributed under the terms of the CC BYNC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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