Biblio

This paper is concered with the nonlinear cyber physical system (CPS) with uncertain parameters under false data injection (FDI) attacks. The interval type-2 (IT2) fuzzy model is utilized to approximate the nonlinear system, then the nonlinear system can be represented as a convex combination of linear systems. To detect the FDI attacks, a novel robust fuzzy extended state observer with H∞ preformance is proposed, where the fuzzy rules are utilized to the observer to estimate the FDI attacks. Utilizing the observation of the FDI attacks, a security control scheme is proposed in this paper, in which a compensator is designed to offset the FDI attacks. Simulation examples are given to illustrate the effecitveness of the proposed security scheme.

In this paper, we use selective laser melting (SLM) technology to fabricate AlNiCo magnetic materials, and the effects of laser processing parameters on the density and mechanical properties of AlNiCo magnetic materials were studied. We tested the magnetic properties of the heat-treated magnets. The results show that both laser power and scanning speed affect the forming. In this paper, the influence of laser power on the density of samples far exceeds the scanning speed. Through the experiment, we obtained the optimal range of process parameters: laser power (150 170W) and laser scanning speed (800 1000mm/s). Although the samples formed within this range have higher density, there are still many cracks, further research work should be done.

People are increasingly sharing their details online as internet usage grows. Therefore, fraudsters have access to a massive amount of information and financial activities. The attackers create web pages that seem like reputable sites and transmit the malevolent content to victims to get them to provide subtle information. Prevailing phishing security measures are inadequate for detecting new phishing assaults. To accomplish this aim, objective to meet for this research is to analyses and compare phishing website and legitimate by analyzing the data collected from open-source platforms through a survey. Another objective for this research is to propose a method to detect fake sites using Decision Tree and Random Forest approaches. Microsoft Form has been utilized to carry out the survey with 30 participants. Majority of the participants have poor awareness and phishing attack and does not obverse the features of interface before accessing the search browser. With the data collection, this survey supports the purpose of identifying the best phishing website detection where Decision Tree and Random Forest were trained and tested. In achieving high number of feature importance detection and accuracy rate, the result demonstrates that Random Forest has the best performance in phishing website detection compared to Decision Tree.

A Quick Response (QR) Code is a type of barcode that can be read by the digital devices and which stores the information in a square-shaped. The QR Code readers can extract data from the patterns which are presented in the QR Code matrix. A QR Code can be acting as an attack vector that can harm indirectly. In such case a QR Code can carry malicious or phishing URLs and redirect users to a site which is well conceived by the attacker and pretends to be an authorized one. Once the QR Code is decoded the commands are triggered and executed, causing damage to information, operating system and other possible sequence the attacker expects to gain. In this paper, a new model for QR Code authentication and phishing detection has been presented. The proposed model will be able to detect the phishing and malicious URLs in the process of the QR Code validation as well as to prevent the user from validating it. The development of this application will help to prevent users from being tricked by the harmful QR Codes.

Data driven classification models are useful to assess quality of manufactured electronics. Because decisions are taken based on the models, their veracity is relevant, covering aspects such as accuracy, transparency and clarity. The proposed BB-Stepwise algorithm aims to improve the classification model transparency and accuracy of black box models. K-Nearest Neighbours (KNN) is a black box model which is easy to implement and has achieved good classification performance in different applications. In this paper KNN-Stepwise is illustrated for fault detection of electronics devices. The results achieved shows that the proposed algorithm was able to improve the accuracy, veracity and transparency of KNN models and achieve higher transparency and clarity, and at least similar accuracy than when using Decision Tree models.