Towards Scalable Defenses against Intimate Partner Infiltrations
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913889479819264 |
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| author | Yang, Weisi Liu, Shinan Xiao, Feng Feamster, Nick Xia, Stephen |
| author_facet | Yang, Weisi Liu, Shinan Xiao, Feng Feamster, Nick Xia, Stephen |
| contents | 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 calibration phase upon installation enables AID to adapt to each user's behavioral patterns, achieving high accuracy with minimal false alarms. Our 27-participant user study demonstrates that AID achieves highly accurate detection of non-owner access and fine-grained IPI-related activities, attaining a false positive rate of 1.6%, which is 11x lower than existing methods, and an end-to-end F1 score of 0.981. These findings suggest that AID can serve as a forensic tool that security clinics can deploy to scale their ability to identify IPI tactics and deliver personalized, far-reaching support to survivors. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03682 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Scalable Defenses against Intimate Partner Infiltrations Yang, Weisi Liu, Shinan Xiao, Feng Feamster, Nick Xia, Stephen Cryptography and Security Human-Computer Interaction 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 calibration phase upon installation enables AID to adapt to each user's behavioral patterns, achieving high accuracy with minimal false alarms. Our 27-participant user study demonstrates that AID achieves highly accurate detection of non-owner access and fine-grained IPI-related activities, attaining a false positive rate of 1.6%, which is 11x lower than existing methods, and an end-to-end F1 score of 0.981. These findings suggest that AID can serve as a forensic tool that security clinics can deploy to scale their ability to identify IPI tactics and deliver personalized, far-reaching support to survivors. |
| title | Towards Scalable Defenses against Intimate Partner Infiltrations |
| topic | Cryptography and Security Human-Computer Interaction |
| url | https://arxiv.org/abs/2502.03682 |