Abstract: The utilization of machine learning has significant importance in the identification and prevention of distributed denial of service (DDoS) attacks. Through the examination of network traffic patterns, machine learning algorithms possess the capability to detect anomalous activities that serve as indicators of a Distributed Denial of Service (DDoS) assault in a timely manner. This paper presents a novel hybrid approach for the detection of distributed denial-of-service (DDoS) attacks in network logs, leveraging the strengths of both Random Forest (RF) for feature extraction and Recurrent Neural Network (RNN) for classification. The proposed framework harnesses the discriminative power of RF in identifying salient features from the raw network log data, which are subsequently utilized as input for the RNN classifier. The Random Forest algorithm was employed to extract a comprehensive set of discriminative features from the network log data, enabling the model to capture intricate patterns indicative of DDoS attacks. These features were then employed as input to the RNN classifier, facilitating the utilization of sequential dependencies and temporal patterns within the log data. The hybrid model achieved exceptional performance, with an accuracy of 99.99%. Furthermore, the true positive rate was recorded at an impressive 99.99%, demonstrating the model's proficiency in correctly identifying actual instances of DDoS attacks. The false positive rate was exceptionally low, at 0.0001%, underscoring the model's robustness in minimizing misclassifications. This study represents a significant advancement in the field of DDoS attack detection, offering a powerful and accurate solution that effectively combines the strengths of Random Forest for feature extraction and RNN for classification. The hybrid model's outstanding performance metrics affirm its potential for deployment in real-world network security environments, providing a robust defense against DDoS attacks.

Keywords: Distributed Denial of service, Recurrent Neural Network, Random Forest Classifier, Network Logs.

Works Cited:

P.S. Ezekiel, O.E. Taylor " A Hybrid Model for The Detection Of APA-DDoS Attacks Using Random Forest with Recurrent Neural Network ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 12, no. 11, pp. 1-11, 2023. Crossref https://doi.org/10.17148/IJARCCE.2023.121101

PDF | DOI: 10.17148/IJARCCE.2023.121101

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