Privacy Preserving Threat Hunting in Smart Home Environments
Ahmed M. Elmisery, Mirela Sertovic

TL;DR
This paper introduces a privacy-preserving distributed threat hunting method for smart home environments, enabling threat detection while protecting homeowners' sensitive usage logs, and demonstrates its effectiveness through a scenario with experimental results.
Contribution
It proposes a novel distributed threat hunting approach that preserves privacy, allowing threat class composition without revealing individual logs, tailored for smart home environments.
Findings
Effective threat detection in smart homes with privacy preservation
Successful scenario demonstration with experimental results
Enhanced cooperation among stakeholders without compromising privacy
Abstract
The recent proliferation of smart home environments offers new and transformative circumstances for various domains with a commitment to enhancing the quality of life and experience. Most of these environments combine different gadgets offered by multiple stakeholders in a dynamic and decentralized manner, which in turn presents new challenges from the perspective of digital investigation. In addition, a plentiful amount of data records got generated because of the day to day interactions between these gadgets and homeowners, which poses difficulty in managing and analyzing such data. The analysts should endorse new digital investigation approaches to tackle the current limitations in traditional approaches when used in these environments. The digital evidence in such environments can be found inside the records of logfiles that store the historical events occurred inside the smart…
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