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Rule-Based System with Machine Learning Support for Detecting Anomalies in 5G WLANs
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Energy sources evaluation based on multi-criteria decision support approach in China
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Empirical Evaluation of Attacks Against IEEE 802.11 Enterprise Networks: The AWID3 Dataset
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Machine Learning in Network Anomaly Detection: A Survey
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Pick Quality Over Quantity: Expert Feature Selection and Data Preprocessing for 802.11 Intrusion Detection Systems
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Beacon Frame Spoofing Attack Detection in IEEE 802.11 Networks
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Federated Wireless Network Intrusion Detection
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Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset
inferredcites →https://doi.org/10.1109/csnt.2014.158openalex-snapshot
Survey of RTS-CTS Attacks in Wireless Network
inferredcites →https://doi.org/10.1109/iccc51575.2020.9345293openalex-snapshot
CNN-based Intrusion Classification for IEEE 802.11 Wireless Networks
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Wireless Intruder Detection System (WIDS) in Detecting De-Authentication and Disassociation Attacks in IEEE 802.11
inferredcites →https://doi.org/10.1109/icvris.2018.00138openalex-snapshot
Research on Anomaly Intrusion Detection Technology in Wireless Network
inferredcites →https://doi.org/10.1109/indcon.2013.6726015openalex-snapshot
Detection of De-authentication Denial of Service attack in 802.11 networks
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Sensing Data Supported Traffic Flow Prediction via Denoising Schemes and ANN: A Comparison
inferredcites →https://doi.org/10.1109/mines.2012.96openalex-snapshot
Design and Implementation of Lightweight Wireless Lan Intrusion Detection System
inferredcites →https://doi.org/10.1109/msn48538.2019.00079openalex-snapshot
iWEP: An Intelligent WLAN Early Warning Platform Using Edge Computing
inferredcites →https://doi.org/10.1109/smc.2015.55openalex-snapshot
Detection of De-Authentication DoS Attacks in Wi-Fi Networks: A Machine Learning Approach
inferredcites →https://doi.org/10.1109/tifs.2012.2207383openalex-snapshot
Active User-Side Evil Twin Access Point Detection Using Statistical Techniques
inferredcites →https://doi.org/10.1109/tifs.2015.2433898openalex-snapshot
Wireless Anomaly Detection Based on IEEE 802.11 Behavior Analysis
inferredcites →https://doi.org/10.1109/tifs.2017.2762828openalex-snapshot
Deep Abstraction and Weighted Feature Selection for Wi-Fi Impersonation Detection
inferredcites →https://doi.org/10.1109/vtcspring.2019.8746576openalex-snapshot
A Semi-Supervised Learning Approach to IEEE 802.11 Network Anomaly Detection
inferredcites →https://doi.org/10.1109/wcnc.2017.7925567openalex-snapshot
IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach
inferredcites →https://doi.org/10.1145/3507657.3528548openalex-snapshot
On the Robustness of Wi-Fi Deauthentication Countermeasures
inferredcites →https://doi.org/10.30534/ijatcse/2020/1391.32020openalex-snapshot
DARMA: Defeating and Reconnaissance Manna-karma Attacks in 802.11 with Multiple Detections and Prevention
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A Meta-Model to Predict and Detect Malicious Activities in 6G-Structured Wireless Communication Networks
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An Intelligent Intrusion Detection System for 5G-Enabled Internet of Vehicles
inferredcites →https://doi.org/10.3390/electronics9101689openalex-snapshot
A Machine Learning Based Two-Stage Wi-Fi Network Intrusion Detection System
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Quantum particle Swarm optimized extreme learning machine for intrusion detection
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Securing tomorrow: a comprehensive survey on the synergy of Artificial Intelligence and information security
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Machine Learning for Wi-Fi Intrusion Detection: A Comparative Study of Accuracy, Explainability, and Adversarial Robustness
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Artificial Intelligence Approaches for Evil Twin Attack Detection in Modern Wireless Environments: A Review
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