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A Deep Learning-Based Smart Framework for Cyber-Physical and Satellite System Security Threats Detection
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Outgoing claims: the papers this work cites, builds on, or argues with.
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LR-HIDS: logistic regression host-based intrusion detection system for cloud environments
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Autoencoder-based deep metric learning for network intrusion detection
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Network intrusion detection algorithm based on deep neural network
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Distributed Network Intrusion Detection System in Satellite-Terrestrial Integrated Networks Using Federated Learning
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Automatic Clustering of Attacks in Intrusion Detection Systems
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Edge-Detect: Edge-Centric Network Intrusion Detection using Deep Neural Network
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AFRL: Adaptive federated reinforcement learning for intelligent jamming defense in FANET
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MSML: A Novel Multilevel Semi-Supervised Machine Learning Framework for Intrusion Detection System
inferredcites →https://doi.org/10.1109/lnet.2019.2901792openalex-snapshot
On the Feasibility of Deep Learning in Sensor Network Intrusion Detection
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KDD Cup 99 Data Sets: A Perspective on the Role of Data Sets in Network Intrusion Detection Research
inferredcites →https://doi.org/10.1109/sisy.2017.8080566openalex-snapshot
Evaluation of machine learning algorithms for intrusion detection system
inferredcites →https://doi.org/10.1109/tifs.2018.2868614openalex-snapshot
Sequence Covering for Efficient Host-Based Intrusion Detection
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A Survey of Intrusion Detection for In-Vehicle Networks
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From Intrusion Detection to an Intrusion Response System: Fundamentals, Requirements, and Future Directions
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An Enhanced Design of Sparse Autoencoder for Latent Features Extraction Based on Trigonometric Simplexes for Network Intrusion Detection Systems
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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