TY - GEN
T1 - Privacy Preserving Threat Hunting in Smart Home Environments
AU - Elmesiry, Ahmed
AU - Sertovic, Mirela
PY - 2020/1/1
Y1 - 2020/1/1
N2 - 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 of their inhabitants. However, 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 smart home’s gadgets and homeowners, which poses difficulty in managing and analyzing such data. The analysts should endorse new digital investigation approaches and practices to tackle the current limitations in traditional digital investigations when used in these environments. The digital evidence in such environments can be found inside the records of log-files that store the historical events and various actions occurred inside the smart home. Threat hunting can leverage the collective nature of these gadgets, the vengeful artifacts observed on smart home environments can be shared between each other to gain deeper insights into the best way for responding to new threats, which in turn can be valuable in reducing the impact of breaches. Nevertheless, this approach depends mainly on the readiness of smart homeowners to share their own personal usage logs that have been extracted from their smart home environments. However, they might disincline to employ such service due to the sensitive nature of the information logged by their personal gateways. In this paper, we presented an approach to enable smart homeowners to share their usage logs in a privacy-preserving manner. A distributed threat hunting approach has been developed to elicit the various threat reputations with effective privacy guarantees. The proposed approach permits the composition of diverse threat classes without revealing the logged records to other involved parties. Furthermore, a scenario was proposed to depict a proactive threat Intelligence sharing for the detection of potential threats in smart home environments with some experimental results.
AB - 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 of their inhabitants. However, 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 smart home’s gadgets and homeowners, which poses difficulty in managing and analyzing such data. The analysts should endorse new digital investigation approaches and practices to tackle the current limitations in traditional digital investigations when used in these environments. The digital evidence in such environments can be found inside the records of log-files that store the historical events and various actions occurred inside the smart home. Threat hunting can leverage the collective nature of these gadgets, the vengeful artifacts observed on smart home environments can be shared between each other to gain deeper insights into the best way for responding to new threats, which in turn can be valuable in reducing the impact of breaches. Nevertheless, this approach depends mainly on the readiness of smart homeowners to share their own personal usage logs that have been extracted from their smart home environments. However, they might disincline to employ such service due to the sensitive nature of the information logged by their personal gateways. In this paper, we presented an approach to enable smart homeowners to share their usage logs in a privacy-preserving manner. A distributed threat hunting approach has been developed to elicit the various threat reputations with effective privacy guarantees. The proposed approach permits the composition of diverse threat classes without revealing the logged records to other involved parties. Furthermore, a scenario was proposed to depict a proactive threat Intelligence sharing for the detection of potential threats in smart home environments with some experimental results.
KW - Digital investigations
KW - IoT
KW - Privacy
KW - Secure-multiparty computation
KW - Smart home
KW - Threat hunting
U2 - 10.1007/978-981-15-2693-0_8
DO - 10.1007/978-981-15-2693-0_8
M3 - Conference contribution
AN - SCOPUS:85079221838
SN - 9789811526923
T3 - Communications in Computer and Information Science
SP - 104
EP - 120
BT - Advances in Cyber Security - 1st International Conference, ACeS 2019, Revised Selected Papers
A2 - Anbar, Mohammed
A2 - Abdullah, Nibras
A2 - Manickam, Selvakumar
PB - Springer
T2 - 1st International Conference on Advances in Cybersecurity, ACeS 2019
Y2 - 30 July 2019 through 1 August 2019
ER -