An optimal approach to detect anomalies in intrusion detection systems
محورهای موضوعی : شبکه های کامپیوتری
1 - Department of Computer Engineering, Payame Noor University (PNU), Tehran, Iran
کلید واژه: Machine Learning, Security, Intrusion Detection System, Software Defined Network,
چکیده مقاله :
Software Defined Networking (SDN) is considered as an innovate architecture of computer networks by using the central controller. Any modification in network data and its arrangement can be effortlessly executed in software via the controller in these networks. Consequently, the identification and timely response to Distributed Denial of Service (DDoS) attacks can be achieved, which is not the case in conventional networks.This paper uses the α-Entropy statistical method considering a threshold and machine learning techniques, K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) to increase the accuracy of detecting DDoS attacks. In this method, the results are evaluated by 10-fold cross validation. The used dataset is ISOT, CTU-13 and UNB ISCX. The results of evaluation with a precision of 99.84% and FPR value of 0.10% indicate the high efficiency of the proposed model in SDN networks.
Software Defined Networking (SDN) is considered as an innovate architecture of computer networks by using the central controller. Any modification in network data and its arrangement can be effortlessly executed in software via the controller in these networks. Consequently, the identification and timely response to Distributed Denial of Service (DDoS) attacks can be achieved, which is not the case in conventional networks.This paper uses the α-Entropy statistical method considering a threshold and machine learning techniques, K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) to increase the accuracy of detecting DDoS attacks. In this method, the results are evaluated by 10-fold cross validation. The used dataset is ISOT, CTU-13 and UNB ISCX. The results of evaluation with a precision of 99.84% and FPR value of 0.10% indicate the high efficiency of the proposed model in SDN networks.
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