Adaptive Defence of the Internet of Things (IoT) using the Belief-Desire-Intention (BDI) Model for Social Robots

The Internet of Things (IoT) continues to introduce unique challenges and threats to cybersecurity. In parallel, adaptive and autonomous cyber defence has become an emerging research topic leveraging Artificial Intelligence (AI) for cybersecurity solutions that can learn to recognize, mitigate, and respond to cyber-attacks, and evolve over time as the threat surface continues to increase in complexity. This paradigm presents an environment strongly conducive to agent-based systems, which offer a model for autonomous, cooperative, goal-oriented behaviours which can be applied to perform adaptive cyber defence activities. This paper presents a modular applied framework to leverage data models, domain knowledge, and multi-agent architecture to perform adaptive cyber defence capabilities through contextual policy generation and enforcement. The Belief-Desire-Intention (BDI) model is extended for behavioural modeling of agents to perform practical reasoning and deliberation of actions in pursuit of goals

Securing Things in the Healthcare Internet of Things

The Internet of Things (IoT) has had a positive impact on e-health, assisted living, human-centric sensing and wellness. Recently this interconnection has been referred to as Healthcare IoT (H-IoT). Real-time monitoring based on the information gathered from the connected things provides large scale connectivity and a greater insight into patient care, individual habits and routines. While the benefits of introducing this paradigm into healthcare are conspicuous, the underlying security vulnerabilities and threats of the infrastructure and devices cannot go unaddressed. H-IoT is set to impact society significantly, and with attackers already exploiting the IoT in a myriad of ways, it is inevitable that the IoT will become the most vulnerable area of cyber security. Securing these 'things' in H-IoT requires a multi-faceted approach. A multi-agent approach to advanced persistent threat detection in networked systems is conveyed with the use of machine learning for predictive analytics: identifying security vulnerabilities, identifying patterns in order to make predictions and identify outliers