Detection of DDoS Attacks Using Machine Learning Classification Algorithms

The Internet is the most essential tool for communication in today's world. As a result, cyber-attacks are growing more often, and the severity of the consequences has risen as well. Distributed Denial of Service is one of the most effective and costly top five cyber attacks. Distributed Denial...

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Bibliographic Details
Published in:International journal of computer network and information security 2022-12, Vol.14 (6), p.89-97
Main Authors: Dasari, Kishore Babu, Devarakonda, Nagaraju
Format: Article
Language:English
Subjects:
Algorithms
Classification
Cybersecurity
Decision trees
Denial of service attacks
Evaluation
Machine learning
Regression analysis
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Summary:The Internet is the most essential tool for communication in today's world. As a result, cyber-attacks are growing more often, and the severity of the consequences has risen as well. Distributed Denial of Service is one of the most effective and costly top five cyber attacks. Distributed Denial of Service (DDoS) is a type of cyber attack that prevents legitimate users from accessing network system resources. To minimize major damage, quick and accurate DDoS attack detection techniques are essential. To classify target classes, machine learning classification algorithms are faster and more accurate than traditional classification methods. This is a quantitative research applies Logistic Regression, Decision Tree, Random Forest, Ada Boost, Gradient Boost, KNN, and Naive Bayes classification algorithms to detect DDoS attacks on the CIC-DDoS2019 data set, which contains eleven different DDoS attacks each containing 87 features. In addition, evaluated classifiers’ performances in terms of evaluation metrics. Experimental results show that AdaBoost and Gradient Boost algorithms give the best classification results, Logistic Regression, KNN, and Naive Bayes give good classification results, Decision Tree and Random Forest produce poor classification results.
ISSN:2074-9090
2074-9104
DOI:10.5815/ijcnis.2022.06.07