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Please use this identifier to cite or link to this item: http://lrcdrs.bennett.edu.in:80/handle/123456789/237
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dc.contributor.authorMukhopadhyay, Debajyoti-
dc.contributor.authorAhuja, Nisha-
dc.date.accessioned2023-03-22T03:53:25Z-
dc.date.available2023-03-22T03:53:25Z-
dc.date.issued2022-
dc.identifier.citationAhuja, N., Singal, G., Mukhopadhyay, D., & Nehra, A. (2022). Ascertain the efficient machine learning approach to detect different ARP attacks. In Computers and Electrical Engineering (Vol. 99, p. 107757). Elsevier BV.en_US
dc.identifier.issn0045-7906-
dc.identifier.urihttp://lrcdrs.bennett.edu.in:80/handle/123456789/237-
dc.description.abstractSoftware-Defined Networking (SDN) is a programmable network architecture that allows network devices to be controlled remotely, but it is still highly susceptible to traditional attacks such as Address Resolution Protocol (ARP) Poisoning, ARP Flooding, and others. The classification of benign network traffic from ARP Poison and ARP Flooding attacks is presented in this paper employing machine learning (ML) techniques. A python application is developed at the SDN controller using Mininet that collects and logs the features required to detect the attack into a file known as a traffic dataset. This dataset is used to train the ML model and detect the attacks. The hybrid model of Convolution Neural Network-Long Short Term Memory (CNN-LSTM) model out-performs the other ML models with an accuracy score of 99.73%. During the attack, a high CPU utilization of more than 97% and a high memory usage serve as experimental evidence. The attack detection time of 63000 microseconds also demonstrates the efficiency of attack detection.en_US
dc.publisherElsevier Ltden_US
dc.relation.ispartofseries;99-
dc.subjectARP Poison attacken_US
dc.subjectARP Flooding attacken_US
dc.subjectSDNMIen_US
dc.subjectTIMEen_US
dc.subjectMachine learningen_US
dc.titleAscertain the efficient machine learning approach to detect different ARP attacksen_US
dc.typeArticleen_US
dc.indexedscen_US
Appears in Collections:Journal Articles_SCSET

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