Comparative Analysis of Deep Learning Models for Detecting Jamming Attacks in Wi-Fi Network Data
Author:
Affiliation:
1. Marmara University,VeNIT Lab.,Istanbul,Turkey
2. Marmara University,VeNIT Lab.,Dept. of Computer Engineering,Istanbul,Turkey
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10304837/10304840/10304936.pdf?arnumber=10304936
Reference21 articles.
1. Deep learning model-based detection of jamming attacks in low-power and lossy wireless networks
2. The Consequences of Jamming Attacks on Wireless IoT Networks: Evaluating the Performance Metrics in Noiseless and Noisy Environments
3. A Deep Autoencoder Trust Model for Mitigating Jamming Attack in IoT Assisted by Cognitive Radio
4. Intrusion Detection in IoT Networks Using Deep Learning Algorithm
5. Attention is all you need;vaswani;Advances in neural information processing systems,2017
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1. LSTM-Based Jamming Detection and Forecasting Model Using Transport and Application Layer Parameters in Wi-Fi Based IoT Systems;IEEE Access;2024
2. Big Data Streaming and Data Analytics Infrastructure for Efficient AI-Based Processing;Recent Advances in Microelectronics Reliability;2024
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