Abstract
In the evolving world, the drastic expansion of the internet and the use of smart devices demands a change in existing infrastructure. These rapid changes in the structural level also open new dimensions that are susceptible to cyber-attacks. One of the efficient methods to tackle these situations is to apply intelligence to the systems and detect abnormal behaviours. As privacy plays a vital role, here Federated Learning method is used to detect cyber attacks by analysing the data logs and compared with the non-federated learning techniques on the same data. It is very evident from the experiment that Federated Learning is very effective in detecting these attacks by preserving the privacy of the victim organisations/systems.
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Sudhina Kumar, G.K., Krishna Prakasha, K., Muniyal, B. (2023). Intrusion Detection Using Federated Learning. In: Prabhu, S., Pokhrel, S.R., Li, G. (eds) Applications and Techniques in Information Security . ATIS 2022. Communications in Computer and Information Science, vol 1804. Springer, Singapore. https://doi.org/10.1007/978-981-99-2264-2_12
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DOI: https://doi.org/10.1007/978-981-99-2264-2_12
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