Sensitivity Analysis for Vulnerability Mitigation in Hybrid Networks

The development of cyber-assured systems is a challenging task, particularly due to the cost and complexities associated with the modern hybrid networks architectures, as well as the recent advancements in cloud computing. For this reason, the early detection of vulnerabilities and threat strategies are vital for minimising the risks for enterprise networks configured with a variety of node types, which are called hybrid networks. Existing vulnerability assessment techniques are unable to exhaustively analyse all vulnerabilities in modern dynamic IT networks, which utilise a wide range of IoT and industrial control devices (ICS). This could lead to having a less optimal risk evaluation. In this paper, we present a novel framework to analyse the mitigation strategies for a variety of nodes, including traditional IT systems and their dependability on IoT devices, as well as industrial control systems. The framework adopts avoid, reduce, and manage as its core principles in characterising mitigation strategies. Our results confirmed the effectiveness of our mitigation strategy framework, which took node types, their criticality, and the network topology into account. Our results showed that our proposed framework was highly effective at reducing the risks in dynamic and resource constraint environments, in contrast to the existing techniques in the literature.

Spatio-Temporal Pollution Forecasting using Hybrid Networks

Rising real estate prices along with expensive maintenance costs, and lack of spares during times of instrument failure have become major issues for statutory bodies when dealing with real-time pollution monitoring stations. As a possible solution to these problems, a novel class of hybrid spatio-temporal pollution forecasting networks which are a combination of various widely used temporal forecasting methods and spatial interpolation methods have been proposed in this paper. In addition, a novel multi-site Multi Layer Perception based Ensemble method, capable of improving accuracy by taking exogenous variables into account, has also been proposed. Experimental results based on the multi-site air pollution data of Beijing demonstrate that the proposed class of hybrid networks have been effective in predicting the pollution of unknown locations with great levels of accuracy. Moreover, the proposed novel MLP Ensemble method for spatial interpolation has also been empirically shown to perform equivalently in comparison to commonly used spatial interpolation methods.