Deep learning-enhanced anomaly detection for iot security in smart cities

Tarik Himdi; Mohammed Ishaque

Author(s)

Tarik Himdi Jeddah International College,Jeddah,Saudi Arabia

Mohammed Ishaque Jeddah International College,Jeddah,Saudi Arabia

Abstract

The swift expansion of Internet of Things (IoT) devices within smart cities necessitates robust security measures to safeguard critical infrastructure and ensure citizen safety. In response, this research presents an advanced deep learningbased anomaly detection system designed to bolster IoT security within the context of smart cities. Leveraging the IoT-23 dataset, our system demonstrates impressive results. One of the system's notable strengths is its adaptability; it generalizes well to diverse datasets and maintains its efficacy in the presence of adversarial attacks. An intuitive user interface facilitates system management and response to detected anomalies, providing a holistic approach to IoT security in smart cities. Positive user feedback affirms the system's usability and satisfaction, emphasizing its practical utility. This research contributes to the broader field of IoT security. It furnishes well-documented code and resources, laying the groundwork for further advancements in this critical domain. As smart cities continue to evolve, the findings and innovations presented in this research serve as a vital step toward ensuring the integrity, privacy, and reliability of IoT networks within urban environments. Lastly, the findings of the experiments show that this technique has an excellent detection performance, with an accuracy rate which is more than 98.7%.

Publication Details

Page(s) 391-397

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 19, Issue: 6, Year: 2024

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Keywords

deep learning, IoT, LSTM, Intrusion Detection System, smart cities, GRU

References

Tarik Himdi,Ahmed Jawwad .2021 .Cybersecurity challenges during pandemic in smart cities. In 2021 8th International Conference on Computing for Sustainable Global Development (INDIACom), : 445-449.

Al‐Turjman Fadi,Shahroze Ramiz .2022 .An overview of security and privacy in smart cities' IoT communications. Transactions on Emerging Telecommunications Technologies, 33 : .

.2022 .. , 3 : e3677.

.2018 .anomaly detection in smart cities: A laboratory analysis. Sensors, 10 : 3198.

Rathore M.,Mazhar Seungmin,Rho Seungmin .2016 .Urban planning and building smart cities based on the internet of things using big data analytics. Computer networks, 101 : 63-80.

Kamalakanta Sethi,E. Sai Rupesh Sethi,Bera Padmalochan,Madhav Y. Venu .2020 .A context-aware robust intrusion detection system: a reinforcement learning-based approach. International Journal of Information Security, 19 : 657-678.

Nada Abdalgawad,Sajun A.,Kaddoura Y.,Zualkernan Imran A.,Aloul F. .2021 .Generative deep learning to detect cyberattacks for the IoT-. dataset." IEEE Access, 10 : 6430-6441.

Mohammed Ishaque,Hudec Ladislav .2019 .Feature extraction using deep learning for intrusion detection system. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), : 1-5.

Ishaque Mohammed,Yamin Muhammed .2023 .A novel hybrid technique using fuzzy logic, neural networks and genetic algorithm for intrusion detection system. Measurement: Sensors, 30 : 100933.