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Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems
The MIAE model is trained in an unsupervised learning mode to transform the heterogeneous inputs into lower-dimensional representation, which helps classifiers distinguish between normal behaviour and different types of attacks.
Twin Auto-Encoder Model for Learning Separable Representation in Cyberattack Detection
The output of TAE called the \textit{reconstruction representation} is input to downstream models to detect cyberattacks.
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems
Intrusion detection systems (IDSs) play a critical role in protecting billions of IoT devices from malicious attacks.
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