A Federated Network Intrusion Detection System with Multi-Branch Network and Vertical Blocking Aggregation

نویسندگان

چکیده

The rapid development of cloud–fog–edge computing and mobile devices has led to massive amounts data being generated. Also, artificial intelligence technology, like machine learning deep learning, is widely used mine the value data. Specifically, detecting attacks on system using essential. External network press organizations anomaly flow in traffic. intrusion detection (NIDS) been an effective method for flow. However, NIDS hard deploy distributed networks because are kept private. Existing methods cannot obtain accurate under such a federated scenario. To construct while preserving privacy, we propose combined model that integrates binary classifiers into whole based simple classifier specify type attack anomalous offer instruction other security components. We also introduce (FL) our design new aggregation algorithm named vertical blocking (FedVB) according structure. Our experiments demonstrate can be more than multi-classifiers terms accuracy significantly reduce communication computation overhead when applying FedVB.

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ژورنال

عنوان ژورنال: Electronics

سال: 2023

ISSN: ['2079-9292']

DOI: https://doi.org/10.3390/electronics12194049