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https://www.researchgate.net/publication/4019505_Detecting_denial_of_service_attacks_using_support_vector_machines
This paper presents a comparative study of using support vector machines (SVMs), multivariate adaptive regression splines (MARS) and linear genetic programs (LGPs) for detecting denial of service ...
https://www.hindawi.com/journals/jcnc/2019/8012568/
The objectives of this paper are to propose a detection method of DDoS attacks by using SDN based technique that will disturb the legitimate user's activities at the minimum and to propose Advanced Support Vector Machine (ASVM) technique as an enhancement of existing Support Vector Machine (SVM) algorithm to detect DDoS attacks.Cited by: 2
https://ieeexplore.ieee.org/abstract/document/7229711/
Dec 19, 2014 · DDoS detection and analysis in SDN-based environment using support vector machine classifier ... The controller is vulnerable to Distributed Denial of Service (DDoS) attacks that leads to resource exhaustion which causes non-reachability of services given by the controller. The detection of DDoS requires adaptive and accurate classifier that ...
https://www.ripublication.com/ijaer17/ijaerv12n20_92.pdf
detecting DDoS attacks on the client side using SVM (support vector machine). Then we evaluate performance of our method and we make a conclusion and suggest future works. RELATED WORKS . Many of detection method for DDoS attack using machine learning such as Bayesian network, SVM and other algorithms.
http://infonomics-society.org/wp-content/uploads/ijicr/published-papers/volume-5-2014/An-Intelligent-DDoS-Attack-Detection-System-Using-Packet-Analysis-and-Support-Vector-Machine.pdf
implemented the detection system using a support vector machine with the radial basis function (Gaussian) kernel. The detection system is accurate in detecting DDoS attacks. 1. Introduction. In past years, the news about distributed denial of service (DDoS) attack is rapidly increased around the world. Many services of companies and/orCited by: 7
http://ijsrcseit.com/paper/CSEIT1725133.pdf
hosts. Denial of service (DoS) attack consumes the resources of a remote client or network itself, there by denying or degrading the service to the legitimate users. In this paper, we present a system that helps in the detection DoS attacks using the data-mining framework. We have used support vector machine classifier to identify the honey pot
https://link.springer.com/chapter/10.1007/978-3-319-40162-1_4
Jun 01, 2016 · Abstract. The Distributed Denial of Service (DDoS) attacks affect the availability of Web services for an indeterminate period of time, flooding the company’s servers with fraudulent requests and denying requests from legitimate users, generating economic losses by unavailable rendered services.Therefore, the aim of this paper is to show the process of detection prototype DDoS attacks using ...Cited by: 3
https://link.springer.com/chapter/10.1007%2F978-3-540-24677-0_63
Mukkamala, S., Sung, A.H.: Detecting Denial of Service Attacks Using Support Vector Machines. In: Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1231–1236. IEEE Computer Society Press, Los Alamitos (2003) Google ScholarCited by: 7
https://www.researchgate.net/publication/287009238_Detection_and_classification_of_DDOS_attacks_using_machine_learning_algorithms
[9] developed an Alert Classification System using Neural Networks (NNs) and Support Vector Machines (SVM) against Distributed Denial of Service (DDoS) attacks. For simulating a real DDoS attack ...
https://www.irjet.net/archives/V4/i6/IRJET-V4I6200.pdf
Neighbor (KNN), support vector machine (SVM), decision tree and naïve Bayes are described and experimental results by using weka tools are determined. In this paper various denial of service attack types and review of various classification techniques like support vector machine, k-NN, Naïve Bayes and decision tree are given.
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