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dc.contributor.authorBasnet, Diwash Bikram
dc.contributor.authorKholidy, Hisham A.; Advisor
dc.date.accessioned2020-12-21T21:56:45Z
dc.date.available2020-12-21T21:56:45Z
dc.date.issued2020-05
dc.identifier.citationBasnet, D. B., & Kholidy, H. A. (2020). An Empirical Wi-Fi Intrusion Detection System: A Master’s Project Presented to the Department of Network and Computer Security in Partial Fulfillment of the Requirements for the Master of Science Degree. Department of Network and Computer Security, College of Engineering, SUNY Polytechnic Institute.en_US
dc.identifier.urihttp://hdl.handle.net/1951/71333
dc.description.abstractToday, the wireless network devices are growing rapidly, and it is of utmost importance for securing those devices. Attackers or hackers use new methods and techniques to trick the system and steal the most important data. Intrusion Detection Systems detect the attacks by inspecting the network traffics or logs. The work demonstrated the effectiveness of detecting the attacks using machine learning techniques on the AWID dataset, which is produced from real wireless network logging. The author of the AWID dataset may have used several supervised learning models to successfully detect the intrusions. In this paper, we propose a newer approach for intrusion detection model based on dense neural networks, and long short-term memory networks (LSTM) and evaluate the model against the AWID-CLS-R subset. To get the best results from the model, we applied feature selection by replacing the unknown data with the value of “none”, getting rid of all repeated values, and kept only the important features. We did preprocess and feature scaling of both training and testing dataset, additional we also change the 2-dimensional to the 3- dimensional array because LSTM takes an input of 3-dimensional array, and later we used flatten layers to change into a 2-dimensional array for output. A comprehensive evaluation of DNN and LSTM networks are used to classify and predict the attacks and compute the precision, recall, and F1 score. We perform binary classification and multiclass classification on the dataset using neural networks and achieve accuracy ranging from 86.70 % to 96.01%.en_US
dc.publisherSUNY Polytechnic Instituteen_US
dc.subjectwireless networksen_US
dc.subjectcybersecurityen_US
dc.subjecthackersen_US
dc.subjectintrusion detection systemsen_US
dc.subjectmachine learningen_US
dc.subjectneural networksen_US
dc.titleAn Empirical Wi-Fi Intrusion Detection Systemen_US
dc.title.alternativeA Master’s Project Presented to Department of Network and Computer Security in Partial Fulfillment of the Requirements for the Master of Science Degreeen_US
dc.typeOtheren_US


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