Reactive jamming attacks have been considered as one of the most lethal and disruptive threats to subvert or disrupt wireless networks since they attack the broadcast nature of transmission mediums by injecting interfering signals. Existing countermeasures for the Internet against reactive jamming attacks, i.e., channel surfing or frequency hopping, demands excessive computing resources, which are infeasible on the low cost resource constraint of the electrical devices in the Smart Grid wireless mesh networks. Even these are inadequate protect approaches to the control systems where the availability is the major security priority to achieve. To overcome the problems for normal lower computation power electrical devices in the Smart Grid wireless mesh networks with difference security triad from the Internet, we propose an efficient localized jamming-resistant countermeasure against the jamming attacks by the identification of trigger nodes whose wireless signal invokes the jammer in the grid. By constraining the trigger nodes to be receivers only, we can avoid the activation of the jammers and completely nullify the reactive jamming attack. The triggers identification approach utilizes a hexagon tiling coloring and sequential Group Testing (GT), which does not demand any sophisticated hardware. Theoretical analyses and simulation results endorse the suitability of our localized algorithm in terms of message and time complexity.

We propose an effective 2d image based end-to-end deep learning model for malware detection by introducing a black & white embedding to reserve bit information and adapting the convolution architecture. Experimental results show that our proposed scheme can achieve superior performance in both of training and testing data sets compared to well-known image recognition deep learning models (VGG and ResNet).

Since the inherent intermittency and uncertainty of renewable energy resources complicates efficient Microgrid operations, a Demand Response (DR) scheme is implemented for customers in the grid to alter their power-usage patterns. However, for a real-time pricing model at the current DR, the automated decision on the energy price is not trustworthy because of artificial interferences to the power generation. As opposed to energy price, an operational cost-based prosumer scheduling approach would be able to protect the integrity of the power grid operations from deceptive market transactions and assist in robust energy management. To investigate the operational challenges associated with the costs and prosumers in the Microgrid, we focus on formulating the problem mathematically and designing approximation algorithms to solve the problem of how to optimally identify suppliers to minimize the total operational costs associated with providing electricity. We prove the hardness of the scheduling as one of the NP-Hard problems and propose polynomial time algorithms for approximating optimal solutions. With a proper resilience level for reliable power services, the scheduling algorithms include ways to construct not only robust supplier networks, but also group energy communities in terms of black start while minimizing the operational costs. A series of theoretical performances and experimental evaluations also demonstrates the practical effectiveness of this scheduling model for the operations.