Authors - Harshala Shingne, Shwetambari Borade, Dhanashree Hadsul, Aditya D. Nandgirwar, Pranali Pawar, Rupali Vairagade Abstract - Increasing proliferation of networked systems have compounded the necessity to seek effective and non-invasive intrusion detection solutions. The conventional intrusion detecting systems (IDS) are mainly centralized into data aggregation scheme that introduces essential constraints pertaining to data exposure, scalability, and robustness of the system itself. Partially in reaction to this, this paper presents a privacy conscious federated intrusion detection design that allows collinear model training by many network participants in the absence of exchanging raw data. This framework exploits federated learning to create a global intrusion detecting model by continually aggregating local-trained updates, thus retaining the data locality and ownership. In order to achieve high privacy assurances, there are inbuilt secure aggregation mechanisms and perturbation-based mechanisms that accomplish this by avoiding the leakage of sensitive information during model sharing. Additionally, an adaptive-aggregating strategy is proposed that can effectively manipulate non identically distributed data of the participants, as well as improving the generalization process of the global model. Extensive testing on test sets of benchmark intrusion detection has shown that the proposed framework has very high detection rates and much less privacy risk and communication overhead than the traditional centralized techniques. The findings confirm that the framework has the capability of offering a scalable, secure and efficient intrusion detection solution in distributed networks.
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