SQL injection is one of the most popular and serious information security threats. By exploiting database vulnerabilities, attackers may get access to sensitive data or enable compromised computers to conduct further network attacks. Our research is focused on applying machine learning approaches for identication of injection characteristics in the HTTP query string. We compare results from Rule-based Intrusion Detection System, Support Vector Machines, Multilayer Perceptron, Neural Network with Dropout layers, and Deep Sequential Models (Long Short-Term Memory, and Gated Recurrent Units) using multiple string analysis, bag-of-word techniques, and word embedding for query string vectorization. Results proved benets of applying machine learning approach for detection malicious pattern in HTTP query string.
The compensating choke plays an important role in many high-power industrial applications with reactive power compensation. Due to the high number of devices installed every year and the EU’s efforts to reduce the energy demands of our society, it is advisable to maximize the efficiency of these devices. Due to the non-linearity of the magnetic core, the requirement of a linear operating characteristic, and the presence of a distributed air gap, this is a difficult task, with various technical challenges. This paper presents an analytical method for the electromagnetic design of a three-phase compensating choke with an air-gapped core and a flat load characteristic. The design method considers the fringing magnetic fields and the current-density dimensioning based on an advanced analytical thermal model. The proposed method is based on the use of existing analytical procedures; however, optimization was conducted to achieve a trade-off between the core and the I2R losses to manipulate the efficiency and the weight and identify optimization possibilities. The presented method was verified by the finite element method (FEM) using the engineering-simulation software, ANSYS.
Currently, a significant number of transformers are designed and used in the electricity distribution network. Since these machines are commonly based on an oil-paper electrical insulation system, it is crucial to know how certain important parameters of an oil behave within a wide range of ambient temperatures. Therefore, the range of temperatures should also include low temperature region as these conditions might occur in real application. The aim of this paper is to focus on the temperature dependent viscosity, density, thermal conductivity and specific heat of an oil in order to develop a strongly coupled thermal model of a transformer which is filled with biodegradable natural ester insulation fluid. Obtained results can be well applied within the design, operation and diagnostics phase of these types of electrical machines.
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