Towards Scalable Defenses against Intimate Partner Infiltrations

• URL: http://arxiv.org/abs/2502.03682v1
• Date: Thu, 06 Feb 2025 00:07:08 GMT
• Title: Towards Scalable Defenses against Intimate Partner Infiltrations
• Authors: Weisi Yang, Shinan Liu, Feng Xiao, Nick Feamster, Stephen Xia,
• Abstract summary: Intimate Partner Infiltration (IPI) is a pervasive concern in the United States. Unlike conventional cyberattacks, IPI perpetrators leverage close proximity and personal knowledge to circumvent standard protections. We present AID, an Automated IPI Detection system that continuously monitors for unauthorized access and suspicious behaviors on smartphones.
• Score: 9.694038607827169
• License:
• 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 in the United States, 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 survivors, their scalability remains constrained by resource limitations and the need for specialized counseling. In this paper, we present AID, an Automated IPI Detection system that continuously monitors for unauthorized access and suspicious behaviors on smartphones. AID employs a two-stage 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 an end-to-end top-3 F1 score of 0.981 with a false positive rate of 4%. These findings suggest that AID can serve as a forensic tool within security clinics, scaling their ability to identify IPI tactics and deliver personalized, far-reaching support to survivors.

Related papers

• CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in Collaborative Perception [53.088988929450494]
  Collaborative perception (CP) is a promising method for safe connected and autonomous driving. We propose a new paradigm for malicious agent detection that effectively identifies malicious agents at the feature level. We also develop a robust defense method called CP-Guard+, which enhances the margin between the representations of benign and malicious features.
  arXiv  Detail & Related papers  (2025-02-07T12:58:45Z)
• Autonomous Identity-Based Threat Segmentation in Zero Trust Architectures [4.169915659794567]
  Zero Trust Architectures (ZTA) fundamentally redefine network security by adopting a "trust nothing, verify everything" approach. This research applies the proposed AI-driven, autonomous, identity-based threat segmentation in ZTA.
  arXiv  Detail & Related papers  (2025-01-10T15:35:02Z)
• IDU-Detector: A Synergistic Framework for Robust Masquerader Attack Detection [3.3821216642235608]
  In the digital age, users store personal data in corporate databases, making data security central to enterprise management. Given the extensive attack surface, assets face challenges like weak authentication, vulnerabilities, and malware. We introduce the IDU-Detector, integrating Intrusion Detection Systems (IDS) with User and Entity Behavior Analytics (UEBA) This integration monitors unauthorized access, bridges system gaps, ensures continuous monitoring, and enhances threat identification.
  arXiv  Detail & Related papers  (2024-11-09T13:03:29Z)
• Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition [5.955900146668931]
  Recent research has shown high accuracy in recognizing subjects or gender from radar gait patterns, raising privacy concerns. This study addresses these issues by investigating privacy vulnerabilities in radar-based Human Activity Recognition (HAR) systems. We propose a novel method for privacy preservation using Differential Privacy (DP) driven by attributions derived with Integrated Decision Gradient (IDG) algorithm.
  arXiv  Detail & Related papers  (2024-11-04T14:08:26Z)
• Federated Anomaly Detection for Early-Stage Diagnosis of Autism Spectrum Disorders using Serious Game Data [0.0]
  This study presents a novel semi-supervised approach for ASD detection using AutoEncoder-based Machine Learning (ML) methods. Our approach utilizes data collected manually through a serious game specifically designed for this purpose. Since the sensitive data collected by the gamified application are susceptible to privacy leakage, we developed a Federated Learning framework.
  arXiv  Detail & Related papers  (2024-10-25T23:00:12Z)
• Malicious Agent Detection for Robust Multi-Agent Collaborative Perception [52.261231738242266]
  Multi-agent collaborative (MAC) perception is more vulnerable to adversarial attacks than single-agent perception. We propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception. We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X.
  arXiv  Detail & Related papers  (2023-10-18T11:36:42Z)
• TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly Detection [59.04634695294402]
  Video anomaly detection (VAD) without human monitoring is a complex computer vision task. Privacy leakage in VAD allows models to pick up and amplify unnecessary biases related to people's personal information. We propose TeD-SPAD, a privacy-aware video anomaly detection framework that destroys visual private information in a self-supervised manner.
  arXiv  Detail & Related papers  (2023-08-21T22:42:55Z)
• Attention-Enhanced Deep Learning for Device-Free Through-the-Wall Presence Detection Using Indoor WiFi Systems [9.087163485833054]
  We propose a novel system for human presence detection using the channel state information (CSI) of WiFi signals. Our system named attention-enhanced deep learning for presence detection (ALPD) employs an attention mechanism to automatically select informative subcarriers from the CSI data. We evaluate the proposed ALPD system by deploying a pair of WiFi access points (APs) for collecting CSI dataset, which is further compared with several benchmarks.
  arXiv  Detail & Related papers  (2023-04-25T19:17:36Z)
• Face Presentation Attack Detection [59.05779913403134]
  Face recognition technology has been widely used in daily interactive applications such as checking-in and mobile payment. However, its vulnerability to presentation attacks (PAs) limits its reliable use in ultra-secure applicational scenarios.
  arXiv  Detail & Related papers  (2022-12-07T14:51:17Z)
• Epidemic mitigation by statistical inference from contact tracing data [61.04165571425021]
  We develop Bayesian inference methods to estimate the risk that an individual is infected. We propose to use probabilistic risk estimation in order to optimize testing and quarantining strategies for the control of an epidemic. Our approaches translate into fully distributed algorithms that only require communication between individuals who have recently been in contact.
  arXiv  Detail & Related papers  (2020-09-20T12:24:45Z)
• Decentralized Privacy-Preserving Proximity Tracing [50.27258414960402]
  DP3T provides a technological foundation to help slow the spread of SARS-CoV-2. System aims to minimise privacy and security risks for individuals and communities.
  arXiv  Detail & Related papers  (2020-05-25T12:32:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.