Identification of Attack Traffic Using Machine Learning in Smart IoT Networks
Author:
Affiliation:
1. Guangzhou University, Guangzhou, China
2. University of Swabi, Swabi, Pakistan
3. Harbin Institute of Technology, Harbin, China
Abstract
Publisher
Hindawi Limited
Subject
Computer Networks and Communications,Information Systems
Link
http://downloads.hindawi.com/journals/scn/2022/9804596.pdf
Reference10 articles.
1. Feature Extraction Method for Hidden Information in Audio Streams Based on HM-EMD
2. DAM-SE: A Blockchain-Based Optimized Solution for the Counterattacks in the Internet of Federated Learning Systems
3. Poor Coding Leads to DoS Attack and Security Issues in Web Applications for Sensors
4. AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment
5. Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city
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1. Acoustic Signature Analysis for Distinguishing Human vs. Synthetic Voices in Vishing Attacks;2023 8th International Conference on Information Technology Research (ICITR);2023-12-07
2. Machine Learning-based Traffic Classification and Channel Allocation in IoT: A Survey;2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA);2023-08-18
3. A Hybrid Feature Selection Approach based on Random Forest and Particle Swarm Optimization for IoT Network Traffic Analysis;International Journal of Electrical and Electronics Research;2023-06-30
4. LPCOCN: A Layered Paddy Crop Optimization-Based Capsule Network Approach for Anomaly Detection at IoT Edge;Information;2022-12-16
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