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An evolutionary KNN model for DDoS assault detection using genetic algorithm based optimization

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Abstract

Distributed Denial of Service (DDoS) attacks continue to pose a significant threat to network infrastructures, exploiting vulnerabilities within existing security protocols and disrupting the seamless availability of online services. The intricate interconnections of nodes within computer networks contribute to the dynamic structure of this environment, complicating efforts to establish a secure and productive user experience. Effectively mitigating DDoS attacks in this complex networked setting remains a challenge. While current strategies primarily rely on anomaly detection and signature-based techniques, utilizing statistical analysis and predefined patterns to identify and thwart attacks, none have consistently demonstrated efficacy or reliability. Consequently, there is a compelling need for advancements in security mechanisms to address DDoS threats more effectively. This research introduces an innovative and highly efficient approach that incorporates various classification algorithms, including Random Forest, Decision Tree, Gradient Boosting, Linear SVM, Logistics, K-nearest neighbors (KNN), and AdaBoost, for DDoS attack detection. The performance of these machine learning classifiers is evaluated using key metrics such as accuracy, recall, F1-score, and precision. Remarkably, experimental results reveal outstanding accuracy rates, with Random Forest achieving the highest accuracy in detecting attacks. Additionally, a genetic algorithm is employed to select optimal features from the dataset, further enhancing the performance of the classifiers. This results in a notable 25% increase in accuracy, surpassing AdaBoost and Logistics, with K-nearest neighbors emerging as the top performer in terms of accuracy.

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Funding

This project is partly funded by National Research Council of Thailand (NRCT), Project no. N42A660902.

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Authors and Affiliations

  1. Department of Information Technology, Indira Gandhi Delhi Technical University for Women, Kashmere Gate, Delhi, India

    Fizza Rizvi & Nonita Sharma

  2. Department of Computer Science and Engineering, Dr. BR Ambedkar NIT, Jalandhar, Punjab, India

    Ravi Sharma

  3. Department of Computer Science and Engineering, Lovely Professional University, Punjab, India

    Manik Rakhra

  4. College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia

    Arwa N. Aledaily & Kusum Yadav

  5. Business Information Technology Division, Department of Statistics, Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok, Thailand

    Wattana Viriyasitavat

  6. Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon

    Gaurav Dhiman

  7. Centre of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, India

    Gaurav Dhiman

  8. Department of Computer Science and Engineering, Graphic Era Deemed to Be University, Dehradun, 248002, India

    Gaurav Dhiman

  9. Division of Research and Development, Lovely Professional University, Phagwara, India

    Gaurav Dhiman

  10. MEU Research Unit, Middle East University, Amman, Jordan

    Gaurav Dhiman

  11. Department of Computer Engineering, NIT Kurukshetra, Kurukshetra, India

    Amandeep Kaur

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  1. Fizza Rizvi
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Rizvi, F., Sharma, R., Sharma, N. et al. An evolutionary KNN model for DDoS assault detection using genetic algorithm based optimization. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18744-5

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