FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386070114> ?p ?o ?g. }

• W4386070114 endingPage "14" @default.
• W4386070114 startingPage "1" @default.
• W4386070114 abstract "Technology development brought numerous lifestyle changes. People move around with smart gadgets and devices in the home, work environment, and familiar places. The Internet acts as a backbone for all applications and connecting multiple devices to set up a smart environment is technically termed as IoT (Internet of Things). The feature merits of IoT are explored in numerous fields from simple psychical data measurement to complex trajectory data measurement. Where the place is inaccessible to humans, IoT devices are used to analyze the region. Though IoT provides numerous benefits, due to its size and energy limitations, it faces security and privacy issues. Intrusions in IoT networks have become common due to these limitations and various intrusion detection methods are introduced in the past decade. Existing learning-based methods lag in performance while detecting multiple attacks. Conventional detection models could not be able to detect the intrusion type in detail. The diverse IoT network data has several types of high dimensional features which could not be effectively processed by the conventional methods while detecting intrusions. Recently improvements in learning strategies proved the performance of deep learning models in intrusion detection systems. However, detecting multiple attacks using a single deep learning model is quite complex. Thus, in this research a multi deep learning model is presented to detect multiple attacks. The initial intrusion features are extracted through the AlexNet, and then essential features are selected through bidirectional LSTM. Finally, the selected features are classified using the decision tree C5.0 algorithm to attain better detection accuracy. Proposed model experimentations include benchmark NSL-KDD dataset to verify performances and compared the results with existing IDSs based on DeepNet, Multi-CNN, Auto Encoder, Gaussian mixture, Generative adversarial Network, and Convolutional Neural Network models. The proposed model attained maximum detection accuracy of 98.8% over conventional methods. Overall, an average of 15% improved detection performance is attained by the proposed model in detecting several types of intrusions in the IoT network." @default.
• W4386070114 created "2023-08-23" @default.
• W4386070114 creator A5082887355 @default.
• W4386070114 creator A5083060281 @default.
• W4386070114 date "2023-08-19" @default.
• W4386070114 modified "2023-10-14" @default.
• W4386070114 title "Hybrid intrusion detection model for Internet of Things (IoT) network environment" @default.
• W4386070114 cites W2099940443 @default.
• W4386070114 cites W2557450880 @default.
• W4386070114 cites W2806697149 @default.
• W4386070114 cites W2911505293 @default.
• W4386070114 cites W2921134108 @default.
• W4386070114 cites W2923817828 @default.
• W4386070114 cites W2929049293 @default.
• W4386070114 cites W2955014922 @default.
• W4386070114 cites W2990495437 @default.
• W4386070114 cites W2998722477 @default.
• W4386070114 cites W3003685271 @default.
• W4386070114 cites W3086410687 @default.
• W4386070114 cites W3086698253 @default.
• W4386070114 cites W3131438039 @default.
• W4386070114 cites W3135011374 @default.
• W4386070114 cites W3154485088 @default.
• W4386070114 cites W3175217968 @default.
• W4386070114 cites W3179240416 @default.
• W4386070114 cites W3189826552 @default.
• W4386070114 cites W3192519096 @default.
• W4386070114 cites W3195027245 @default.
• W4386070114 cites W3196743383 @default.
• W4386070114 cites W3204466840 @default.
• W4386070114 cites W3213644780 @default.
• W4386070114 cites W3217748719 @default.
• W4386070114 cites W4205599972 @default.
• W4386070114 cites W4205943221 @default.
• W4386070114 cites W4206739656 @default.
• W4386070114 cites W4206773083 @default.
• W4386070114 cites W4210260155 @default.
• W4386070114 cites W4213182863 @default.
• W4386070114 cites W4214629996 @default.
• W4386070114 cites W4214752923 @default.
• W4386070114 cites W4224126807 @default.
• W4386070114 cites W4224331011 @default.
• W4386070114 cites W4226125749 @default.
• W4386070114 cites W4229459202 @default.
• W4386070114 cites W4283215393 @default.
• W4386070114 cites W4284683049 @default.
• W4386070114 cites W4285106051 @default.
• W4386070114 cites W4285127639 @default.
• W4386070114 cites W4285294415 @default.
• W4386070114 cites W4286373821 @default.
• W4386070114 cites W4288391281 @default.
• W4386070114 cites W4292004502 @default.
• W4386070114 cites W4296019153 @default.
• W4386070114 cites W4307021520 @default.
• W4386070114 cites W4312847977 @default.
• W4386070114 cites W4313180313 @default.
• W4386070114 cites W4320008781 @default.
• W4386070114 cites W4365815661 @default.
• W4386070114 cites W4367047386 @default.
• W4386070114 cites W4380574979 @default.
• W4386070114 doi "https://doi.org/10.3233/jifs-233575" @default.
• W4386070114 hasPublicationYear "2023" @default.
• W4386070114 type Work @default.
• W4386070114 citedByCount "0" @default.
• W4386070114 crossrefType "journal-article" @default.
• W4386070114 hasAuthorship W4386070114A5082887355 @default.
• W4386070114 hasAuthorship W4386070114A5083060281 @default.
• W4386070114 hasConcept C108583219 @default.
• W4386070114 hasConcept C110875604 @default.
• W4386070114 hasConcept C119857082 @default.
• W4386070114 hasConcept C124101348 @default.
• W4386070114 hasConcept C136764020 @default.
• W4386070114 hasConcept C138885662 @default.
• W4386070114 hasConcept C154945302 @default.
• W4386070114 hasConcept C2776401178 @default.
• W4386070114 hasConcept C35525427 @default.
• W4386070114 hasConcept C38652104 @default.
• W4386070114 hasConcept C41008148 @default.
• W4386070114 hasConcept C41895202 @default.
• W4386070114 hasConcept C81860439 @default.
• W4386070114 hasConcept C84525736 @default.
• W4386070114 hasConceptScore W4386070114C108583219 @default.
• W4386070114 hasConceptScore W4386070114C110875604 @default.
• W4386070114 hasConceptScore W4386070114C119857082 @default.
• W4386070114 hasConceptScore W4386070114C124101348 @default.
• W4386070114 hasConceptScore W4386070114C136764020 @default.
• W4386070114 hasConceptScore W4386070114C138885662 @default.
• W4386070114 hasConceptScore W4386070114C154945302 @default.
• W4386070114 hasConceptScore W4386070114C2776401178 @default.
• W4386070114 hasConceptScore W4386070114C35525427 @default.
• W4386070114 hasConceptScore W4386070114C38652104 @default.
• W4386070114 hasConceptScore W4386070114C41008148 @default.
• W4386070114 hasConceptScore W4386070114C41895202 @default.
• W4386070114 hasConceptScore W4386070114C81860439 @default.
• W4386070114 hasConceptScore W4386070114C84525736 @default.
• W4386070114 hasLocation W43860701141 @default.
• W4386070114 hasOpenAccess W4386070114 @default.
• W4386070114 hasPrimaryLocation W43860701141 @default.

• next