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Smart power grids suffer from coordinated attacks that exploit both their physical and cyber layers. It is an inevitable requirement to study power systems under such complex hacking scenarios to discover the system vulnerabilities and protect against those vulnerabilities and their attack vectors with appropriate defensive actions. This paper proposes an efficient tri-level programming framework that dynamically determines attacking scenarios, along with the best defensive actions in cyber-physical systems. A tri-level optimisation framework is proposed to fit the optimal decisions of defence strategies, malicious vectors and their operators for optimising the unmet demand while launching hacking behaviours. Defending resource allocation is designed using an evolutionary algorithm to examine coordinated attacks that exploit power systems. The proposed framework includes a Genetic Algorithm (GA) to solve each of the model levels in power systems. This proposed framework can flexibly model malicious vectors and their defences. IEEE 14-bus benchmark is employed to evaluate the proposed framework.

In this paper, we propose a new attribute reduction strategy based on rough sets and grey wolf optimization (GWO). Rough sets have been used as an attribute reduction technique with much success, but current hill-climbing rough set approaches to attribute reduction are inconvenient at finding optimal reductions as no perfect heuristic can guarantee optimality. Otherwise, complete searches are not feasible for even medium sized datasets. So, stochastic approaches provide a promising attribute reduction technique. Like Genetic Algorithms, GWO is a new evolutionary computation technique, mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. The grey wolf optimization find optimal regions of the complex search space through the interaction of individuals in the population. Compared with GAs, GWO does not need complex operators such as crossover and mutation, it requires only primitive and easy mathematical operators, and is computationally inexpensive in terms of both memory and runtime. Experimentation is carried out, using UCI data, which compares the proposed algorithm with a GA-based approach and other deterministic rough set reduction algorithms. The results show that GWO is efficient for rough set-based attribute reduction.

This article presents a feature selection and classification system for 2D brain tumors from Magnetic resonance imaging (MRI) images. The proposed feature selection and classification approach consists of four main phases. Firstly, clustering phase that applies the K-means clustering algorithm on 2D brain tumors slices. Secondly, feature extraction phase that extracts the optimum feature subset via using the brightness and circularity ratio. Thirdly, reduct generation phase that uses rough set based on power set tree algorithm to choose the reduct. Finally, classification phase that applies Multilayer Perceptron Neural Network algorithm on the reduct. Experimental results showed that the proposed classification approach achieved a high recognition rate compared to other classifiers including Naive Bayes, AD-tree and BF-tree.