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Title INTRUSION DETECTION USING DEEP LEARNING IN IOT-BASED SMART HEALTHCARE

Author(s) Ana Shahid

Abstract Title: Intrusion Detection using Deep Learning in IoT based Smart Healthcare The rapid increase and implementation of Internet of things (IoT) based technologies in healthcare have made a significant contribution to the global network. Despite bringing useful benefits all over the globe such as real-time monitoring of patients’ information and diagnosing properly whenever needed, Internet of things (IoT) based systems appear to be an easy target for intruders. As the number of threats and attacks against IoT devices and services rapidly increases, the security of Internet of Things (IoT) in healthcare has become more challenging. In order to meet this challenge, hybrid learning based effective Intrusion Detection in IoT needs to be developed. In this study, we propose a novel hybrid model for intrusion detection in IoT based smart healthcare using RF, SVM, LSTM and gradient boosting. We proposes generalized model by handling the problems of overfitting and underfitting. We generates a new feature to make the proposed model more effective for detecting intrusion in IoT. We study the performance of proposed model in multi classification using MQTT-IOT-IDS 2020 dataset, a latest dataset with IoT network traces and compared the performance with different ML and DL algorithms. Experimental results show that our model performs better intrusion detection than other DL and ML algorithms.

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2023-02-16

Subject Computer Science

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