Biblio

Both the sophistication and scale of cyberattacks are increasing, revealing the extent of risks at which critical infrastructure and other information and communication systems are exposed. Furthermore, the introduction of IoT devices in a number of different applications, ranging from home automation to the monitoring of critical infrastructure, has created an even more complicated cybersecurity landscape. A large amount of research has been done on detecting these attacks in real time, however mitigation is left to security experts, which is time consuming and may have economic consequences. In addition, there is no public data available for action selection that could enable the use of the latest techniques in machine learning or deep learning for this area. Currently, most systems deploy a rule-based response selection methodology for mitigating detected attacks. In this paper, we introduce a situation calculus-based approach to automated response for IoT cyberattacks. The approach offers explicit semantic-rich cognitive modeling of attacks, effects and actions and supports situation inference for timely and accurate responses. We demonstrate the effectiveness of our approach for modelling and responding to cyberattacks by implementing a use case in a real-world IoT scenario.

IoT systems continue to grow in scale and exhibit similarities to complex systems seen in nature and biology: Systems are composed of heterogeneous entities (mobile devices, servers, sensors, data items, databases, etc.) coordinated in a Cloud environment forming a digital eco-system. Properties of such systems include variety, emergent outcome, self-organisation, etc. The scale of IoT systems, and the disparity in the capabilities of the devices on the market, means there needs to be a unifying model to enable a secure and assured interaction among those `things'. The authors propose conceptual designs for an efficient architecture, run-time decision models using assured models for such an interaction in a digital eco-system. This is done using the situation calculus modelling to represent the fundamental requirements for adjustable decentralised feedback control mechanisms necessary for the IoT-ready software systems: It is shown that complex properties and emergent outcomes of the system can be deduced, emanating from the simple distributed interaction models. A case study from the rail industry is used to assess the design and possible implementation.