Gabriel Sanchez-Perez

arXiv2026

Measuring likelihood in cybersecurity

Cybersecurity risk is commonly expressed through impact and likelihood, yet likelihood remains difficult to estimate because cyber incidents are underreported, heterogeneous datasets are weakly comparable, and attacker behaviour changes faster than conventional probability baselines. This article proposes a pipeline for operationalizing likelihood through a cyber exposure profile that integrates external cyber knowledge and organization specific telemetry into a graph based representation. The contribution is a formally specified artifact chain, from unified data model through organization specific profiling, metric registry, likelihood scoring, and control prioritization, that operationalizes four constructs grounded in incident evidence: Exposure, Traceability, Motivation, and Systems Update. The pipeline provides a pathway from heterogeneous source evidence to a bounded likelihood indicator comparable across organizations and observation periods. An evaluation in 15 real organizations shows that those implementing the cyber exposure profile were associated with reduced incident frequency and faster detection and response times, providing preliminary empirical support for the framework directional claims.

cs.CR↗

arXiv2020

IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition

In this paper, we introduce a new benchmark dataset named IPN Hand with sufficient size, variety, and real-world elements able to train and evaluate deep neural networks. This dataset contains more than 4,000 gesture samples and 800,000 RGB frames from 50 distinct subjects. We design 13 different static and dynamic gestures focused on interaction with touchless screens. We especially consider the scenario when continuous gestures are performed without transition states, and when subjects perform natural movements with their hands as non-gesture actions. Gestures were collected from about 30 diverse scenes, with real-world variation in background and illumination. With our dataset, the performance of three 3D-CNN models is evaluated on the tasks of isolated and continuous real-time HGR. Furthermore, we analyze the possibility of increasing the recognition accuracy by adding multiple modalities derived from RGB frames, i.e., optical flow and semantic segmentation, while keeping the real-time performance of the 3D-CNN model. Our empirical study also provides a comparison with the publicly available nvGesture (NVIDIA) dataset. The experimental results show that the state-of-the-art ResNext-101 model decreases about 30% accuracy when using our real-world dataset, demonstrating that the IPN Hand dataset can be used as a benchmark, and may help the community to step forward in the continuous HGR. Our dataset and pre-trained models used in the evaluation are publicly available at https://github.com/GibranBenitez/IPN-hand.

cs.CV↗