Cognitive solutions to surveillance in security (CSSS)

公共安全和网络防御监控对于保护国家关键基础设施和公共场所免受恐怖主义、内乱、犯罪活动和网络攻击的威胁至关重要。依靠人工操作员实时监控多个屏幕和信息源已经并将导致未被发现的事件，从而增加对地方和国家安全的威胁。尽管监控设备取得了进步，操作员仍然面临许多认知挑战（例如信息过载、多任务处理、背景声音分散注意力、干扰和疲劳），这些挑战给操作员的认知系统带来压力并降低效率。因此，开发以用户为中心的自适应智能系统对于确保最佳监控工作至关重要。在这个项目中，我们采用跨学科方法，将系统工程与认知神经科学相结合，以确保对问题空间进行彻底、基于经验的表征，然后测试创新解决方案以改善安全和网络防御监视工作。使用完善的认知范式，我们寻求建立薄弱的功能点，从而确定需要支持的领域。为此，我们将使用动态演变的人群场景或网络入侵警报的多屏幕监视沉浸式环境来创建受控实验，这在外部现实主义和经验控制之间提供了最佳折衷。最初的实证工作将有助于制定一套标准，提供敏感的诊断指标，用于评估不同系统支持绩效的能力，进而用于开发和测试决策支持系统和创新培训程序的原型。

Surveillance in public security and cyber-defence is essential to protecting the country's critical infrastructure and public places against the threat of terrorism, civil unrest, criminal activity, and cyber-attacks. Reliance on human operators to monitor multiple screens and sources of information in real-time has and will lead to undetected incidents, thus increasing the threat to local and national security. Despite advances in surveillance equipment, operators are still faced with many cognitive challenges (e.g., information overload, multitasking, background sound distraction, interruptions, and fatigue) which place stresses on the operator's cognitive system and reduce efficiency. Hence, the development of user-centered adaptive, intelligent systems becomes critical to ensure optimal surveillance work. In this project we adopt an interdisciplinary approach by combining system engineering with cognitive neuroscience to ensure a thorough, empirically-based characterisation of the problem space and then test innovative solutions to improve security and cyber-defence surveillance work. Using well-established cognitive paradigms we seek to establish weak functional points and thus identify areas that need support. To do so, we will create controlled experiments using multi-screen surveillance immersive environments of dynamically evolving crowd scenes or cyber-intrusions alerts, which provide an optimal compromise between external realism and empirical control. The initial empirical work will help develop a set of criteria that will provide sensitive diagnostic metrics with which to assess the ability of different systems to support performance, which in turn will be used to develop and test prototypes of decision support systems and innovative training procedures.