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Network Intrusion Detection Using Machine Learning & Deep Learning

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dc.contributor.advisor Dr. Mohammad Arifuzzaman
dc.contributor.author Labonno, Meherunesa
dc.contributor.author Ahmed, Sabbir
dc.date.accessioned 2023-02-09T07:10:30Z
dc.date.available 2023-02-09T07:10:30Z
dc.date.issued 2023-02-05
dc.identifier.uri http://dspace.ewubd.edu:8080/handle/123456789/3872
dc.description This thesis submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Information and Communication Engineering of East West University, Dhaka, Bangladesh en_US
dc.description.abstract In recent decades, rapid development in the world of technology and networks has been achieved, also there is a spread of Internet services in all fields over the world. Piracy numbers have increased, also a lot of modern systems were penetrated, so the developing information security technologies to detect the new attack become an important requirement. One of the most important information security technologies is an Intrusion Detection System (IDS) that uses machine learning and deep learning techniques to detect anomalies in the network. The main idea of this paper is to use an advanced intrusion detection system with high network performance to detect the unknown attack package. We use different kind of machine learning algorithm with high accuracy to detect which attack is the most in these dataset. In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS). A DNN with 0.1 rate of learning is applied and is run for 100 number of epochs and KDDCup-‘99‘ dataset has been used for training and benchmarking the network. We compare between both of them on the same dataset . en_US
dc.language.iso en_US en_US
dc.publisher East West University en_US
dc.relation.ispartofseries ;ECE00264
dc.subject Network Intrusion Detection, Machine Learning & Deep Learning, Intrusion Detection System en_US
dc.title Network Intrusion Detection Using Machine Learning & Deep Learning en_US
dc.type Thesis en_US


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