Detection and Mitigation of Distributed Denial of Service Attacks on Network Architecture  Software Defined Networking Using the Naive Bayes Algorithm

Munir, M.S.; Ardiansyah, Ipung; Santoso, Joko; Mustopa, Ali; Mulyatun, Sri

doi:10.24076/joism.2022v3i2.656

Cited by 2 publications

(1 citation statement)

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“…Detecting and mitigating Distributed Denial of Service (DDoS) attacks pose significant challenges, particularly in the context of Software-Defined Networking (SDN) systems [3]. Researchers have investigated multiple methodologies, such as machine learning, deep learning, and anomaly detection, in order to enhance the security of networks against distributed denial-of-service (DDoS) attacks [4] [5] [6]. Moreover, there has been a considerable amount of research conducted on the effects of Distributed Denial of Service (DDoS) attacks on new technologies like the Internet of Things (IoT) and cloud computing [7] [8].…”

Section: Introductionmentioning

confidence: 99%

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2024

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER AND COMM

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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 denialof-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.

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