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Title DEEP LEARNING FOR INTRUSION DETECTION IN IOT BASED SMART HOMES

Author(s) Nazia Butt

Abstract Title: Deep Learning for Intrusion Detection in IOT based Smart Homes Scurrying growth in IOT has been alleviating the different fields like Health Care Units, Industrial Units, Smart Homes or Military and so is trending topic for research. However, with the emergence of IOT, there is also high risk of security violations. Security breach involved the different categories of attack, illegitimate access and other privacy risks in IOT systems. Therefore, different researches had been conducted to palliate Cyber-attacks by configuring Intrusion Detection in different scenarios but as attacks are also growing with the same rate therefore, more work is still demanded or expected. In the proposed study, the comparative analysis of different Anomaly Based Intrusion detection system is conducted concerning existing state-of-the-art studies with respect to datasets, Machine Learning and Deep learning models. To overcome the limitations highlighted in existing work, the research proposed a novel solution for anomaly based intrusion detection in IOT with increased performance, lessen overfitting/underfitting issues and generalizable in nature. To ensure high performance w.r.t. different evaluation metrics, hybridization of Machine learning and Deep Learning models LSTM, KNN and DT was done and implemented on real time dataset CIC-IDS-IOT2022. To avoid underfitting/overfitting issues, feature selection and hyperparameter tuning was implemented. To check its impact, same solution was tested on benchmark dataset UNSW-NB15. Google Colab and python were used as a platform and language. Experiment results showed significant increase in performance while minimizing misclassification and other limitations in comparison with state-of-the-art solutions. Involvement of more datasets and hybridization of other ML/DL algorithms inspired by the proposed solution in real time IOT-IDS network is a future research goal.

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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