Network Security Threat Detection in IoT-Enabled Smart Cities

Authors

  • Nirup Kumar Reddy Pothireddy   Independent Researcher, USA

DOI:

https://doi.org/10.32628/IJSRST22548678

Keywords:

IoT Security, Smart Cities, Cyber Threat Detection, Intrusion Detection Systems (IDS), Machine Learning in Cybersecurity, Blockchain for IoT Security, Network Anomaly Detection, Data Privacy in IoT, Edge Computing Security, DDoS Mitigation in Smart Cities.

Abstract

Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One-Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much more security resilience, decreasing possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must. Since security threats in IoT-enabled smart cities may not appear clear and present to detection mechanisms, efforts have been made to use artificial intelligence methods for anomaly detection. Anomaly detection has been performed using unsupervised learning approaches (Autoencoders, GANs, One-Class SVMs) in turn, with these instances considered security threats. In addition, an element for patches and traffic redirection in real time is included in the framework. Results show that the AI detection in general has much more security resilience, decreasing possible attack vectors. This makes the integration of various features like AI, blockchain, and IDS for a solid IoT security a must.

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Published

2022-07-14

Issue

Section

Research Articles

How to Cite

[1]
Nirup Kumar Reddy Pothireddy "Network Security Threat Detection in IoT-Enabled Smart Cities" International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 4, pp.784-799, July-August-2022. Available at doi : https://doi.org/10.32628/IJSRST22548678