چكيده به لاتين
Recently, smart grid has emerged as a promising solution in the next generation power grid system. These networks provide better power quality, accessibility and security by using ICTs. Many telecommunications technologies proposed for the smart grid, are vulnerable to cyber-attacks. These vulnerabilities can lead to the lack of reliability of system performance, unnecessary costs and even sequential disasters for energy producers and users. One of the most import vulnerabilities of Smart Grid is damage to the integrity of network data. False Data Injection Attack is one of the most severe attacks with the aim of disrupting the integrity of the smart grid. Based on false data, the control centers take wrong decisions about network state, which can lead to economic costs and even global blackout. Machine learning based attack detection methods are recently discovered by researchers to detect these attacks. In these approaches, measured data by sensors of the network are considered as inputs of machine learning algorithms. The proposed algorithms used to extract the data characteristics and categorize them into two secure and attacked groups. For example KNN, SVM, DBM, and etc. has been suggested and these algorithms have performed well compared with previous ones, but they still have challenges such as: curse of dimensionality, pre-training requirement, lack of comprehensiveness, and the high computational complexity.
In this thesis we use the properties of smart grid such as: Line continuity, voltage and current dependence, state estimation mechanism and so on to propose 3 methods to overcome some of the challenges of false data injection attack detection in smart grid. In the first method we propose DKNN algorithm. In addition to its high accuracy and speed, this algorithm is easily implemented in control centers due to its simple operation. We use the convex optimization method in this algorithm which can extract data properties and categorize them without high computational load on the system. In second proposed method we suggest to use Deep KNN method for FDI attack detection in smart grid. This algorithm composed of autoencoder and KNN. Autoencoder has 4 hidden layer which KNN used implicitly in each layer. Using this algorithm increases the accuracy, precision and recall of decisions and prevents wrong alarms. The third proposed approach is recurrent neural network that makes real-time decisions about the categorization of data. This feature allows the attack to be detected in less 2 miliseconds and prevents its progress. In this algorithm, the LSTM cell is used to prevent gradient decent.
