Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India

Research Article

Real-time Visualization and Classification of DDoS Attack using Supervised Learning Algorithms

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  • @INPROCEEDINGS{10.4108/eai.23-11-2023.2343234,
        author={Subbulakshmi  T and Arun Santhosh R A and Mohith  G K and Suganya  R and Girish  Subramanian and Shubh K Patel and Baraiya Manit Rameshkumar},
        title={  Real-time Visualization and Classification of DDoS Attack using Supervised Learning Algorithms},
        proceedings={Proceedings of the 1st International Conference on Artificial Intelligence, Communication, IoT, Data Engineering and Security, IACIDS 2023, 23-25 November 2023, Lavasa, Pune, India},
        publisher={EAI},
        proceedings_a={IACIDS},
        year={2024},
        month={3},
        keywords={ddos attacks machine learning intrusion detection random forest logistic regression k-nearest neighbors confusion matrix},
        doi={10.4108/eai.23-11-2023.2343234}
    }
    
  • Subbulakshmi T
    Arun Santhosh R A
    Mohith G K
    Suganya R
    Girish Subramanian
    Shubh K Patel
    Baraiya Manit Rameshkumar
    Year: 2024
    Real-time Visualization and Classification of DDoS Attack using Supervised Learning Algorithms
    IACIDS
    EAI
    DOI: 10.4108/eai.23-11-2023.2343234
Subbulakshmi T1,*, Arun Santhosh R A1, Mohith G K1, Suganya R1, Girish Subramanian2, Shubh K Patel1, Baraiya Manit Rameshkumar1
  • 1: School of Computer Science and Engineering,Vellore Institute of Technology,Chennai
  • 2: Pennsylvania State University
*Contact email: research.subbulakshmi@gmail.com

Abstract

With increased reliance on the Internet for various services, Distributed Denial of Service (DDoS) attacks are a major concern for organizations. DDoS attacks causes significant damage to the target system, leading to service downtime, data loss, and financial losses. To mitigate the impact of DDoS attacks, effective detection mechanisms are necessary. In this paper, a machine learning-based approach for DDoS attack detection is proposed. NSL-KDD Dataset of network traffic data containing both normal and attack traffic were used and various visualization techniques and machine learning models, including Random Forest Classifier, K- Nearest Neighbours Classifier and Logistic Regression were applied. The performance of these models was evaluated using cross-validation and their accuracies were measured. The experimental results show that Random Forest Classifier outperforms other models with an accuracy of 99.33% before and after applying post pruning method. The experiment also focused on the false positives and false negatives and the implications of the results were discussed.