Towards Scalable Defenses against Intimate Partner Infiltrations
Weisi Yang, Shinan Liu, Feng Xiao, Nick Feamster, Stephen Xia

TL;DR
This paper introduces AID, an automated system for detecting unauthorized access and intimate partner infiltration on smartphones, achieving high accuracy and low false positives to aid scalable victim support.
Contribution
The paper presents AID, a novel multimodal, privacy-preserving detection system that adapts to individual behaviors for effective IPI identification on smartphones.
Findings
Achieves a false positive rate of 1.6%, 11x lower than existing methods.
Attains an end-to-end F1 score of 0.981.
Demonstrates high accuracy in detecting non-owner access and IPI activities.
Abstract
Intimate Partner Infiltration (IPI)--a type of Intimate Partner Violence (IPV) that typically requires physical access to a victim's device--is a pervasive concern around the world, often manifesting through digital surveillance, control, and monitoring. Unlike conventional cyberattacks, IPI perpetrators leverage close proximity and personal knowledge to circumvent standard protections, underscoring the need for targeted interventions. While security clinics and other human-centered approaches effectively tailor solutions for victims, their scalability remains constrained by resource limitations and the need for specialized counseling. We present AID, an Automated IPI Detection system that continuously monitors for unauthorized access and suspicious behaviors on smartphones. AID employs a unified architecture to process multimodal signals stealthily and preserve user privacy. A brief…
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Taxonomy
TopicsMarriage and Sexual Relationships
