Abstract
Machine learning is a subfield of artificial intelligence that focuses on making predictions about some outcome based on information from a dataset. In cybersecurity, machine learning is often used to improve intrusion detection systems and identify trends in data that could indicate an oncoming cyber attack. Data privacy is an extremely important aspect of cybersecurity, and there are many industries that have more demanding laws to ensure the security of user data. Due to these regulations, machine learning algorithms can not be widely utilized in these industries to improve outcomes and accuracy of predictions. However, federated learning is a recent development in the field of machine learning that allows for the training of a model using decentralized data. Federated learning is a practical solution in cases where a machine learning model needs to be trained with data from different servers, devices, or organizations and the data from one party can not be shared with the other parties. Federated learning is also a form of cybersecurity in itself, as it improves the security of machine learning models in terms of data privacy. This paper explains the concept of federated learning and its specific applications to cybersecurity, with a focus on federated learning’s impact on the healthcare industry. Cyber threats to machine learning models as well as recent improvements in federated learning algorithms and their implications in the field of cybersecurity are also discussed.
Document Type
Paper
Disciplines
Artificial Intelligence and Robotics | Information Security
DOI
10.25776/3txx-xd95
Publication Date
12-15-2022
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Federated Learning and Applications in Cybersecurity
Machine learning is a subfield of artificial intelligence that focuses on making predictions about some outcome based on information from a dataset. In cybersecurity, machine learning is often used to improve intrusion detection systems and identify trends in data that could indicate an oncoming cyber attack. Data privacy is an extremely important aspect of cybersecurity, and there are many industries that have more demanding laws to ensure the security of user data. Due to these regulations, machine learning algorithms can not be widely utilized in these industries to improve outcomes and accuracy of predictions. However, federated learning is a recent development in the field of machine learning that allows for the training of a model using decentralized data. Federated learning is a practical solution in cases where a machine learning model needs to be trained with data from different servers, devices, or organizations and the data from one party can not be shared with the other parties. Federated learning is also a form of cybersecurity in itself, as it improves the security of machine learning models in terms of data privacy. This paper explains the concept of federated learning and its specific applications to cybersecurity, with a focus on federated learning’s impact on the healthcare industry. Cyber threats to machine learning models as well as recent improvements in federated learning algorithms and their implications in the field of cybersecurity are also discussed.