Approach For Quick Detection Research Articles

Lightweight URL Phishing Detection System Using SVM and Similarity

Abstract: As a criminal offense of employing technical method to thieve sensitive information of customers, phishing is presently an important risk facing the net, and losses because of phishing are developing regularly. function engineering is essential in phishing website detection answers; however, the accuracy of detection critically relies upon on earlier knowledge of features. furthermore, even though features extracted for one-of-a-kind dimensions are more comprehensive, a disadvantage is that extracting those functions calls for a massive amount of time. To address those barriers, we propose a multidimensional characteristic phishing detection method based on a quick detection approach by way of the usage of deep mastering (MFPD). within the first step, character sequence functions of the given URL are extracted and used for brief class by means of deep mastering, and this step does not require 1/3-party assistance or any prior know-how approximately phishing. inside the 2d step, we combine URL statistical functions, webpage code features, website text capabilities and the short classification end result of deep studying into multi-dimensional features. The technique can lessen the detection time for putting a threshold.

A Quick Detection Approach of Magnetic Iron Grade for Magnetite

In order to achieve aims of rapid real-time monitoring iron grade during sales process or mineral processing for the iron ores, a quick detection approach to magnetic iron grade of magnetite based on electromagnetic induction is explored in the paper. Information acquisition head is focused on an iron grade sensor, and the iron grade sensor is made up of coil-windings with magnetite core. Information acquisition circuits are given adapting mode of the transformer bridge type rectifier, and construction of the head is designed and machined. Testing mechanism is analyzed from the angle of magnetic field analysis, and the magnetic field model is also built up. Furthermore, magnetic iron grade of some testing samples are measured by the detection device above. It is showed that when the grade range of the magnetic iron of magnetite is varied from 20% to 65.3%, deviation is lower than 1.0%. By research above, it is shown that the detection method of iron grade based on electromagnetic induction is feasible, and it is also significant for improving the total testing level of mineral processing fields.

A Bayesian network approach for fixture fault diagnosis in launch of the assembly process

Verification and correction of faults related to tooling design and tooling installation are important in the auto body assembly process launch. This paper introduces a Bayesian network (BN) approach for quick detection and localisation of assembly fixture faults based on the complete measurement data set. Optimal sensor placement for effective diagnosis of multiple faults, structure learning of the Bayesian network and the diagnostic procedure are incorporated in the proposed approach. The effective independence sensor placement method is used to reach the desired number of optimal sensor locations, which provide the concise and effective sensor nodes to build the diagnostic Bayesian network. A new algorithm based on conditional mutual information tests is put forward to learn the Bayesian network structure. The body side assembly case was used to illustrate the suggested method and the simulation analysis was performed to evaluate the effectiveness of the diagnostic network. The work demonstrated that the proposed methodology composes a feasible and powerful tool for fixture fault diagnosis in launch of the assembly process.