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- W4382564609 abstract "With the advent of a paradigm shift in the area of data communication, Internet of things (IoT) has remarkably transformed the whole facets of information sharing and data aggregation. It comprises of numerous heterogeneous devices and sensors having different protocols and standards which exchange and collect data over the Internet with robust connection. The extensive usage of these devices poses a vast number of cyber security threats as these devices are subjected to diverse range of attacks due to low power, computational and memory limitations. Various security frameworks and mechanisms have been presented to defend these attacks from time to time. Intrusion Detection System are dedicated hardware or computer program-based frameworks to protect these devices from cyber and malicious threats. In this paper, we have evaluated different deep learning algorithms on state-of-the-art IoT datasets and it has been observed that deep learning-based techniques have shown sufficient potential in the identification and classification of different network attack patterns in IoT devices." @default.
- W4382564609 created "2023-06-30" @default.
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- W4382564609 date "2023-01-01" @default.
- W4382564609 modified "2023-09-27" @default.
- W4382564609 title "Deep Learning-Based Recognition and Classification of Different Network Attack Patterns in IoT Devices" @default.
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- W4382564609 doi "https://doi.org/10.1007/978-981-99-1946-8_2" @default.
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