Towards an integrated risk analysis security framework according to a systematic analysis of existing proposals

The information society depends increasingly on risk assessment and management systems as means to adequately protect its key information assets.The availability of these systems is now vital for the protection and evolution of companies.However,several factors have led to an increasing need for more accurate risk analysis approaches.These are:the speed at which technologies evolve,their global impact and the growing requirement for companies to collaborate.Risk analysis processes must consequently adapt to these new circumstances and new technological paradigms.The objective of this paper is,therefore,to present the results of an exhaustive analysis of the techniques and methods offered by the scientific community with the aim of identifying their main weaknesses and providing a new risk assessment and management process.This analysis was carried out using the systematic review protocol and found that these proposals do not fully meet these new needs.The paper also presents a summary of MARISMA,the risk analysis and management framework designed by our research group.The basis of our framework is the main existing risk standards and proposals,and it seeks to address the weaknesses found in these proposals.MARISMA is in a process of continuous improvement,as is being applied by customers in several European and American countries.It consists of a risk data management module,a methodology for its systematic application and a tool that automates the process.

Identification of Key Links in Electric Power Operation Based-Spatiotemporal Mixing Convolution Neural Network

As the scale of the power system continues to expand,the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately.Therefore,more reliable and accurate security control methods are urgently needed.In order to improve the accuracy and reliability of the operation risk management and control method,this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network.To provide early warning and control of targeted risks,first,the video stream is framed adaptively according to the pixel changes in the video stream.Then,the optimized MobileNet is used to extract the feature map of the video stream,which contains both time-series and static spatial scene information.The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes.Finally,training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study,and the proposed algorithm is compared with the unimproved MobileNet.The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation,and has good real-time performance.The average accuracy of the algorithm can reach 87.8%,and the frame rate is 61 frames/s,which is of great significance for improving the reliability and accuracy of security control methods.