Securing Digital Lives: Personalized Measures for Comprehensive Data Protection for Home Networks
DOI:
https://doi.org/10.47611/jsrhs.v13i2.6748Keywords:
home network security, federated learning, data privacy, automated protectionAbstract
This research paper addresses the increasing cyber threats targeting home networks and the lack of comprehensive security solutions tailored for consumers. It proposes a comprehensive solution that addresses data protection, threat monitoring, and impact mitigation, which could be automated through AI and Federated Learning (FL) to simplify security for non-experts. The paper conducts a literature review and qualitative analysis to examine cyber threats and security research, proposing an architecture that utilizes FL across home devices. It discusses challenges such as handling biases and diverse devices, limitations of interoperability, and hardware requirements. The paper emphasizes the urgent need for automated and personalized cybersecurity tailored for consumers to address the escalating threats to home networks, arguing that FL has the potential to enable this while addressing privacy concerns associated with centralized AI.
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Copyright (c) 2024 Rishi Vora; Sarada Prasad Gochhayat, Virgel Torremocha, Jothsna Kethar
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