Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems

arXiv:2403.15511v1 Announce Type: new
Abstract: While intrusion detection systems (IDSs) benefit from the diversity and generalization of IoT data features, the data diversity (e.g., the heterogeneity and high dimensions of data) also makes it difficult to train effective machine learning models in IoT IDSs. This also leads to potentially redundant/noisy features that may decrease the accuracy of the detection engine in IDSs. This paper first introduces a novel neural network architecture called Multiple-Input Auto-Encoder (MIAE). MIAE consists of multiple sub-encoders …

abstract arxiv auto benefit cs.ai cs.lg data data diversity data features detection dimensions diversity encoder feature features feature selection idss iot leads machine machine learning machine learning models multiple systems train type