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• W3197461719 abstract "The evolution of computing is increasing in a vast manner that will integrate many physical objects and the internet to generate a new interconnection, such as the Internet of Things (IoT). It is estimated that the number of devices that will be interconnected to the internet will be more than trillions until 2025. Due to the lack of interoperability when these devices are interconnected in a vast heterogeneous network, it is tough to define and apply security mechanisms. The IoT networks have been exposed to many vulnerable attacks that disturb the network. Therefore, designing an intrusion detection system that provides additional security tools specific to IoT is needed to apply security mechanisms to detect the attacks in the network. In this paper, we propose a novel hybrid GA-CMIM machine learning algorithm that improves the efficiency in detecting the botnet intrusions with the set of optimal features that are selected from the dataset using a feature selection method." @default.
• W3197461719 created "2021-09-13" @default.
• W3197461719 creator A5029673673 @default.
• W3197461719 creator A5054097928 @default.
• W3197461719 date "2021-08-31" @default.
• W3197461719 modified "2023-10-16" @default.
• W3197461719 title "Genetic Algorithm-Conditional Mutual Information Maximization based feature selection for Bot Attack Classification in IoT devices" @default.
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• W3197461719 doi "https://doi.org/10.13052/jmm1550-4646.1816" @default.
• W3197461719 hasPublicationYear "2021" @default.
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