Towards Using Inductive Learning to Adapt Security Controls in Smart Homes

Kushal Ramkumar, Wanling Cai, John C. McCarthy, Gavin Doherty, Bashar Nuseibeh, Liliana Pasquale

Abstract: Smart home users often lack the technical expertise required to secure their devices and could benefit from the automated selection of security controls. In this paper, we explore the capabilities of inductive learning to adapt the requirements and system specification of a smart home system to identify security controls. We present preliminary results from using Inductive Learning via Answer Set Programming (ILASP) to learn how to produce (1) an updated system specification that enables benign behaviours while excluding malicious ones and (2) updated security requirements that the system should satisfy. We encode traces of benign and malicious execution traces from two smart home attack datasets (CICIoT2023 and IoT-23) into ILASP's language. ILASP could learn updated system specifications (to prevent DoS/Botnet attacks), new security requirements (to check for malware uploads and insecure protocols), and other integrity constraints that could be indicators of compromise. However, challenges remain when ILASP cannot perform the learning due to its sensitive syntax or complex system behaviour that lead to a large analysis space. Finally, we discuss how these limitations can be addressed in future work.

External IDs:dblp:conf/seams/RamkumarC0DNP25