Abstract
Cloud computing provides various services, like computation power, storage, platforms for developing applications, and software over the Internet. These services must be accessible to all its users all the time throughout the globe. Distributed denial of service (DDoS) attacks can be used by the attackers to disrupt the accessibility of these services. In these types of attacks, several systems are used to consume network or server resources that eventually results in inaccessibility of cloud services. In this paper, we propose a technique for detecting DDoS attacks using an artificial neural network (ANN) model. The model is trained using the imperialistic competitive algorithm. NSL KDD dataset is used to evaluate the performance of the proposed technique. Our proposed technique gives detection accuracy of 83.5% and 65% with KDDTest+ and KDDTest-21 datasets, respectively. Performance comparison with some other machine learning-based techniques and state-of-the-art techniques is also presented.
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Kushwah, G.S., Ranga, V. (2022). DDoS Attacks Detection in Cloud Computing Using ANN and Imperialistic Competitive Algorithm. In: Dubey, H.M., Pandit, M., Srivastava, L., Panigrahi, B.K. (eds) Artificial Intelligence and Sustainable Computing. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-1220-6_22
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DOI: https://doi.org/10.1007/978-981-16-1220-6_22
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