Investigation on Efficient Machine Learning Algorithm for DDoS Attack Detection

Devi, R. Sahila; Bharathi, R.; Kumar, P. Krishna

doi:10.1109/iccece51049.2023.10085248

Cited by 7 publications

(2 citation statements)

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“…DDoS Attacks are inevitable to detect with one appropriate and generalized machine learning algorithms and the authors have investigated popular machine learning methods on the CICDoS2019 dataset with the direction that the hybrid algorithms to be tested for better performance in the future work [1]. In this work, authors have identified the DDoS attack, specifically the Ping of Death attack by the Random Forest with accuracy value equal to 0.998 [2].…”

Section: Related Workmentioning

confidence: 99%

“…In this paper, author presents DDoS attack of flood types: UDP, HTTP, SYN. NTP, Zero Day attack and worked on the classification of these attacks by using techniques such as Gaussian Naïve Bayes, The Stochastic Gradient Descent (SGD), Random Forest, Support Vector Machine, K-Nearest Neighbours [1]. There are two types of DDoS defense attacks: defense of source and defense of destination [5].…”

Section: Introductionmentioning

confidence: 99%

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The Investigative Study on the Performance Analysis of SMOTE employed Machine Learning Classifier Models to DDoS Attack Detection

Sravan Kumar G

2023

IJRITCC

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Distributed Denial of Service (DDoS) attack, a severe attack on the network services during the contemporary era, is categorized under active attacks in security attacks. The impact of this attack on the organization or individual resources leads to massive loss in terms of finance, reputation. Therefore, detecting Distributed DDoS attacks is vital in ensuring the availability and integrity of online services of an organization. The work in this paper employed machine learning techniques, complemented by Synthetic Minority Over-sampling Technique (SMOTE), to tackle the inherent challenge of imbalanced DDoS attack dataset: CSE-CIC-2018 and to enhance computational efficiency while maintaining accuracy with a fraction of the original dataset. The emphasis of the this works is to comprehensively assess the performance of five prominent algorithms of machine learning - Naive Bayes, Random Forest, Logistic Regression, Decision Tree, and XGBoost - in the context of detection of DDoS attack. The overhead of oversampling is handled with the application of SMOTE oversampling and it has been addressed data imbalance issues, improving the algorithms' capability to identify attacks of DDoS effectively. The work of this paper finds and reveals distinct comparative advantages among the algorithms employed in the DDoS attack detection and provides actionable insights in choosing the most suitable algorithms of Machine learning for the detection of DDoS attack, provided emphasizing the significance of SMOTE to enhance the algorithms' performance in the presence of imbalanced data. Eventually, this paper offers invaluable guidance for organizations seeking to make safe their network against DDoS attacks while considering the crucial tradeoffs between accuracy and computational efficiency. The proposed work in this paper presented the results that Random Forest classifier ensured the better performance with F1-Score value 0.99, Mathews Correlation Coefficient (MCC) value 0.98 and accuracy value 0.99 relative to other classifiers employed.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%