Real-time Predictive Analytics for Physical Security

EasyChair Preprint 14580

Abstract

In an increasingly interconnected world, ensuring physical security across various sectors is becoming more complex and critical. Real-time predictive analytics offers a transformative solution by leveraging advanced algorithms, machine learning, and big data to predict and mitigate security threats before they occur. This paper explores the integration of real-time data streams from diverse sources, such as surveillance systems, sensors, access controls, and social media feeds, to enhance situational awareness in physical security environments. By applying predictive models to this data, organizations can identify patterns of abnormal behavior, potential threats, and vulnerabilities, allowing for timely intervention.

The proposed framework utilizes a combination of supervised and unsupervised learning models to classify threats and forecast possible security breaches. The integration of artificial intelligence enables continuous adaptation to evolving threat landscapes, enhancing the accuracy and relevance of predictions. Case studies demonstrate the effectiveness of this approach in various settings, including corporate environments, critical infrastructure, and public spaces.

This research highlights the potential of real-time predictive analytics in reducing response times, minimizing risk, and improving overall security outcomes. However, it also addresses challenges such as data privacy, false positives, and the need for robust infrastructure to support real-time processing. The findings suggest that as predictive analytics technology matures, it will play a pivotal role in the proactive management of physical security threats.

Keyphrases: Computer Science, Cyberthreat, Technology, anomaling

@booklet{EasyChair:14580,
  author    = {Favour Olaoye and Axel Egon},
  title     = {Real-time Predictive Analytics for Physical Security},
  howpublished = {EasyChair Preprint 14580},
  year      = {EasyChair, 2024}}