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Current Update on Management Strategies for Neurological and Psychological disordersCyber Security Intruder Detection Using Deep Learning Approach

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dc.contributor.author Islam, Tariqul
dc.contributor.author Rahman, Md. Mosfikur
dc.contributor.author Saifuzzamam, Mohd.
dc.contributor.author Jabiullah, Md. Ismail
dc.date.accessioned 2024-05-11T10:08:35Z
dc.date.available 2024-05-11T10:08:35Z
dc.date.issued 2022-11-29
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12308
dc.description.abstract Intrusion detection systems (IDS) are among the most promising approaches for securing data and networks; through the years, numerous categorization algorithms have been utilized in IDS. In recent years, as the alarming increase in computer connectivity and the substantial number of applications associated with computer technology have increased, the challenge of cyber security is constantly rising. A proper system of protection for numerous cyber-attacks is also required. This is how incoherence and attacks in a computer network are detected and IDS developed, which could play a possible role in cyber security. The authors used the CICIDS2017 dataset to meet this objective. It is the 2017 set of the Canadian Cyber Security Institute. The authors propose an IDS based on the deep learning technique to increase safety. The purpose was to use a neural network classifier to predict the network and web attacks. en_US
dc.language.iso en_US en_US
dc.publisher Springer en_US
dc.subject Detection systems en_US
dc.subject Cybersecurity en_US
dc.subject Deep learning en_US
dc.subject Networks en_US
dc.title Current Update on Management Strategies for Neurological and Psychological disordersCyber Security Intruder Detection Using Deep Learning Approach en_US
dc.type Article en_US


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