Big Data Analysis-Based Security Situational Awareness for Smart Grid

Abstract

Advanced communications and data processing technologies bring great benefits to the smart grid. However, cybersecurity threats also extend from the information system to the smart grid. The existing security works for smart grid focus on traditional protection and detection methods. However, a lot of threats occur in a very short time and overlooked by exiting security components. These threats usually have huge impacts on smart gird and disturb its normal operation. Moreover, it is too late to take action to defend against the threats once they are detected, and damages could be difficult to repair. To address this issue, this paper proposes a security situational awareness mechanism based on the analysis of big data in the smart grid. Fuzzy cluster based analytical method, game theory and reinforcement learning are integrated seamlessly to perform the security situational analysis for the smart grid. The simulation and experimental results show the advantages of our scheme in terms of high efficiency and low error rate for security situational awareness.

The Improved Hill Encryption Algorithm towards the Unmanned Surface Vessel Video Monitoring System Based on Internet of Things Technology

“…The algorithm is that a × self-invertible matrix is generated as the key matrix of this algorithm, then dividing the original image into × block image. The th pixels of each image block will be grouped together and make up a temporary × image block, it is convenient for future encryption operation [20][21][22][23]. The temporary image matrix is encrypted with the key matrix , transposing the encryption image matrix and double-layer encrypting the matrix and putting the obtained matrix into the th position of the final encryption matrix.…”

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

“…For security threats that occur in short time, a security situational awareness technique was proposed in [299], which uses fuzzy cluster analysis based on game theory and reinforcement learning to enhance security in smart grids. A big data computing architecture for smart grids was proposed [239], [240], [241] [262], [263], [264], [242] Minimizing number of relay transmissions…”

Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for L p -Regularization Using the Multiple Sub-Dictionary Representation

“…The proposed algorithms can be applied in many practical applications [36,37,38,39,40,41,42]. In this section, we conduct some typical applications about sparse image recovery and medical imaging to extend the applications of $L_{1/2}$ and $L_{2/3}$ regularizations and illustrate the excellent robustness and adaptation of the proposed SAITA-$L_p$,$\left(p\in \left\{1/2,2/3\right\}\right)$ algorithm.…”