This work was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number P50NS108676 and Moore Foundation

The increasingly remote workforce resulting from the global coronavirus pandemic has caused unprecedented cybersecurity concerns to organizations. Considerable evidence has shown that one-pass authentication fails to meet security needs when the workforce work from home. The recent advent of continuous authentication (CA) has shown the potential to solve this predicament. In this paper, we propose NF-Heart, a physiological-based CA system utilizing a ballistocardiogram (BCG). The key insight is that the BCG measures the body's micro-movements produced by the recoil force of the body in reaction to the cardiac ejection of blood, and we can infer cardiac biometrics from BCG signals. To measure BCG, we deploy a lightweight accelerometer on an office chair, turning the common chair into a smart continuous identity "scanner". We design multiple stages of signal processing to decompose and transform the distorted BCG signals so that the effects of motion artifacts and dynamic variations are eliminated. User-specific fiducial features are then extracted from the processed BCG signals for authentication. We conduct comprehensive experiments on 105 subjects in terms of verification accuracy, security, robustness, and long-term availability. The results demonstrate that NF-Heart achieves a mean balanced accuracy of 96.45% and a median equal error rate of 3.83% for CA. The proposed signal processing pipeline is effective in addressing various practical disturbances. © 2023 ACM.

A Pose Detection based Continuous Authentication System Design via Gait Feature Analysis

In recent years, with the increasing demand for public safety and the impact of pneumonia (Corona Virus Disease 2019, COVID-19), long-distance, contactless authentication has become a hot topic. Gait recognition technology has broad application prospect in computer vision field because of its ability of long-distance gait recognition and identification verification. On the other hand, with the development of big data, cloud computing, 5G, IoT and other technologies, which makes the Continuous authentication based on cameras is already possible. Therefore, we propose a continuous authentication system based on human pose estimation framework by analyzing and extracting gait characteristics. This system not only has the advantages of easy acquisition, long distance, contactless, and hard to disguise gait recognition, but also has the functions of dynamic authorization and continuous authentication, This method will bring a new development direction for the research of human pose estimation and gait recognition and other related fields. © 2022 IEEE.

BYOZ: Protecting BYOD Through Zero Trust Network Security

As the COVID-19 pandemic scattered businesses and their workforces into new scales of remote work, vital security concerns arose surrounding remote access. Bring Your Own Device (BYOD) also plays a growing role in the ability of companies to support remote workforces. As more enterprises embrace concepts of zero trust in their network security posture, access control policy management problems become a more significant concern as it relates to BYOD security enforcement. This BYOD security policy must enable work from home, but enterprises have a vested interest in maintaining the security of their assets. Therefore, the BYOD security policy must strike a balance between access, security, and privacy, given the personal device use. This paper explores the challenges and opportunities of enabling zero trust in BYOD use cases. We present a BYOD policy specification to enable the zero trust access control known as BYOZ. Accompanying this policy specification, we have designed a network architecture to support enterprise zero trust BYOD use cases through the novel incorporation of continuous authentication & authorization enforcement. We evaluate our architecture through a demo implementation of BYOZ and demonstrate how it can meet the needs of existing enterprise networks using BYOD. © 2022 IEEE.