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A Machine Learning-Based Automated DDoS Malware Attack Detection Scheme
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DOI：10.1007/s40009-026-02133-7.png)
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Abstract
En 中文
In the current era, network environments are essential for human survival but are susceptible to multiple threats like DDoS, phishing, ransomware, and SQL injection, which endanger individuals and organizations. DDoS attacks disrupt service by flooding networks with traffic from various sources, necessitating robust detection methods. While considerable research has utilized machine learning (ML) for attack detection, optimal features and techniques remain undetermined. This paper employs ML methods including artificial neural networks, decision tables, naive Bayes, support vector machines, and ensemble bagging to identify and evaluate novel DDoS detection features. Using datasets such as CICDDoS2019, UNSW-NB15, NSL-KDD, and SDN, the proposed model achieves accuracies of 96.13%, 99.63%, and 99.77% respectively.
Keywords:
Distributed denial of service attack
Machine learning
CICDDoS2019
NSL-KDD
UNSW-NB15
SDN dataset
Journal
N
IF:
1.3
Papers:
520
Citations:
1.1K
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Cited Papers
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IEEE ACCESS
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Proceedings of the 15th International Conference on emerging Networking EXperiments and TechnologiesIF0
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ShareSaveDetecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning ModelsSUSTAINABILITY
OAAI
SUSTAINABILITYIF3.3
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