Marple: Ai-Driven Threat Detection and Response For Securing Soft-Targets

Safeguarding soft targets against diverse threats poses multifaceted chal-lenges, demanding a nuanced security ecosystem that avoids complete hard-ening strategies. It delves into the dynamic realm of i-threats, emphasizing their challenge to security systems, and introduces the concept of "Phyg-ital," merging physical and digital experiences in modern consumer realms. Addressing the unpredictability of crimes or terrorist acts, the research stresses understanding i-threats targeting public spaces. It highlights the evolution of security threats from traditional to cyberspace-driven, prompt-ing innovative policing approaches. The study underscores technology's in-tegration into policing for i-threat management, proposing a taxonomy for machine learning-based predictive policing. It explores social media's role in propagating information and technology-enabled intelligence for faster responses. Within the H2020 APPRAISE project, the MARPLE platform integrates physical and cyber sensors, machine learning, and video analysis tools, empowering law enforcement against potential i-threats to soft tar-gets. This work advocates proactive policing, emphasizing challenges posed by i-threats and the importance of understanding symbolic messages and hate speech to bolster security for public spaces and events