Analyzing effective mitigation of DDoS attack with software defined networking

Dayal, Neelam; Srivastava, Shashank

doi:10.1016/j.cose.2023.103269

Cited by 5 publications

(1 citation statement)

References 8 publications

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“…In the face of this growing threat of DDoS attacks, organizations and states have the option to implement different cybersecurity solutions, such as Digital Attack Map [8], Fortinet [9] and Darktrace [10], among others, which offer advanced real-time monitoring capabilities and ensure multilayered protection to detect and mitigate DDoS attacks [11]. However, it is important to note that these solutions, especially in the case of small-scale networks, tend to involve significant costs [12], largely owing to the need for continuous training and the demand for highly skilled professionals to manage these solutions, which affects their accessibility. On the other hand, other solutions based on traditional approaches, such as traffic protection systems, firewalls, and managed security services, are proving to be increasingly less effective in containing the constant flow of these attacks circulating on the network [13].…”

Section: Introductionmentioning

confidence: 99%

Improvement of Distributed Denial of Service Attack Detection through Machine Learning and Data Processing

Becerra-Suarez,

Fernández-Roman,

Forero

2024

Mathematics

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The early and accurate detection of Distributed Denial of Service (DDoS) attacks is a fundamental area of research to safeguard the integrity and functionality of organizations’ digital ecosystems. Despite the growing importance of neural networks in recent years, the use of classical techniques remains relevant due to their interpretability, speed, resource efficiency, and satisfactory performance. This article presents the results of a comparative analysis of six machine learning techniques, namely, Random Forest (RF), Decision Tree (DT), AdaBoost (ADA), Extreme Gradient Boosting (XGB), Multilayer Perceptron (MLP), and Dense Neural Network (DNN), for classifying DDoS attacks. The CICDDoS2019 dataset was used, which underwent data preprocessing to remove outliers, and 22 features were selected using the Pearson correlation coefficient. The RF classifier achieved the best accuracy rate (99.97%), outperforming other classifiers and even previously published neural network-based techniques. These findings underscore the feasibility and effectiveness of machine learning algorithms in the field of DDoS attack detection, reaffirming their relevance as a valuable tool in advanced cyber defense.

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Section: Introductionmentioning

confidence: 99%