To sustain a clean environment by reducing fossil fuels-based energies and increasing the integration of renewable-based energy sources, i.e., wind and solar power, have become the national policy for many countries. The increasing demand for renewable energy sources, such as wind, has created interest in the economic and technical issues related to the integration into the power grids. Having an intermittent nature and wind generation forecasting is a crucial aspect of ensuring the optimum grid control and design in power plants. Accurate forecasting provides essential information to empower grid operators and system designers in generating an optimal wind power plant, and to balance the power supply and demand. In this paper, we present an extensive review of wind forecasting methods and the artificial neural network (ANN) prolific in this regard. The instrument used to measure wind assimilation is analyzed and discussed, accurately, in studies that were published from May 1st, 2014 to May 1st, 2018. The results of the review demonstrate the increased application of ANN into wind power generation forecasting. Considering the component limitation of other systems, the trend of deploying the ANN and its hybrid systems are more attractive than other individual methods. The review further revealed that high forecasting accuracy could be achieved through proper handling and calibration of the wind-forecasting instrument and method.

Situation awareness (SA) issues necessitate a comprehension of present activities, the ability to forecast, what will happen next, and strategies to assess the threat or impact of current internet activities and projections. These SA procedures are universal, domain-independent and can be used to detect cyber intrusions. This study introduces cyber situation awareness (CSA), its origin, conception, aim, and characteristics based on an analysis of function shortages and development requirements. Furthermore, we discussed the CSA research framework and examined the research history, which is the essential aspect, and assessed the present issues of the research as well. The assessment approaches were divided into three methods: mathematics model, knowledge reasoning, and pattern recognition. The study then goes into detail regarding the core idea, assessment procedure, strengths, and weaknesses of novel approaches, and then, it addresses CSA from three perspectives: model, knowledge representation, and assessment methods. Many common approaches are contrasted, and current CSA application research in the realms of security, transmission, survivability, and system evaluation is discussed. Finally, this study summarized the findings of the present from technical and application systems, outlined CSA’s future development directions, and provided adversary activities and information that can be used to improve an organization’s SA operations.

Computer network security has become increasingly controversial among many businesses as a result of the rise in cyber threats. Artificial neural network (ANN) is mature research in this field, whereas the traditional algorithm is slower, in feedback error, and has the disadvantage of easy convergence to local extreme value. To guide against these threats, in this paper, ANN-back propagation (BP) algorithm is used to establish the relationship between the level of cyber security situational awareness (CSA) and the perceptual parameters and quantitatively evaluate the situational awareness. This study established the ANN-back propagation (BP) to make the relationship between the level of cyber security situational awareness (CSA) and the perceptual parameters, which evaluates situational awareness. The ANN-BP with variable step size learning strategy and simulated annealing method is used for optimization to build a virtual network environment. The proposed model offers better precision, improved sensitivity, and higher (0.987%) accuracy.

In the field of network security situation assessment based on Hidden Markov Models (HMM), evaluation accuracy critically depends on two key factors: the quality of observation sequences and the design of the state transition matrix. Existing methods often rely on randomly sampled observation sequences and expert-defined transition matrices, resulting in reduced adaptability and subjective bias. To address these limitations, this paper proposes an improved HMM-based network security situation assessment approach that integrates alert quality evaluation and game-theoretic modeling. First, the concept of alert quality is introduced to generate observation sequences that better capture real network behaviors. Second, a state transition matrix is constructed using the strategic interactions between attack and defense behaviors, further refined by the probability of attack success to improve reliability and realism. Experimental validation was conducted using the LDoS 1.0 dataset from the Lincoln Laboratory. Comparative analysis with the Haslum, CRNES, and genetic algorithms demonstrates that the proposed method achieves a more stable and accurate estimation of network state probabilities. Specifically, during the intrusion phase, the improved algorithm correctly identified abnormal behavior peaks around 5400 seconds while maintaining a consistent risk value trend aligned with the actual attack scenario. Unlike the Haslum algorithm, which suffers from oversimplified transition probabilities (58% zero transition probabilities), and the genetic algorithm, which exhibits excessive sensitivity due to over-granular alert levels, the proposed model maintains low compromised state probabilities ($\sim$ 0.001), with a realistic rise to 0.14 at 5400 seconds, accurately reflecting the network's gradual degradation. Results confirm that the improved HMM model offers a more rational, interpretable, and quantitatively precise assessment of cybersecurity situations. This approach effectively reduces subjectivity in modeling and enhances the dynamic tracking capability of network security states, providing a reliable foundation for intelligent cyber defense decision-making.