Manuscript Number : IJSRST22548678
Network Security Threat Detection in IoT-Enabled Smart Cities
Authors(1) :-Nirup Kumar Reddy Pothireddy
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.
Nirup Kumar Reddy Pothireddy
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. Publication Details
Published in : Volume 9 | Issue 4 | July-August 2022 Article Preview
Independent Researcher, USA
Date of Publication : 2022-07-14
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 784-799
Manuscript Number : IJSRST22548678
Publisher : Technoscience Academy
Journal URL : https://ijsrst.com/IJSRST22548678
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