IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly vital.We propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data volume.Then the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization capabilities.Experimental results show that the proposed method can efficiently distinguish normal traffic from DDoS traffic.Compared with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

3D laser scanning strategy based on cascaded deep neural network

A 3D laser scanning strategy based on cascaded deep neural network is proposed for the scanning system converted from 2D Lidar with a pitching motion device. The strategy is aimed at moving target detection and monitoring. Combining the device characteristics, the strategy first proposes a cascaded deep neural network, which inputs 2D point cloud, color image and pitching angle. The outputs are target distance and speed classification. And the cross-entropy loss function of network is modified by using focal loss and uniform distribution to improve the recognition accuracy. Then a pitching range and speed model are proposed to determine pitching motion parameters. Finally, the adaptive scanning is realized by integral separate speed PID. The experimental results show that the accuracies of the improved network target detection box, distance and speed classification are 90.17%, 96.87% and 96.97%, respectively. The average speed error of the improved PID is 0.4239°/s, and the average strategy execution time is 0.1521 s.The range and speed model can effectively reduce the collection of useless information and the deformation of the target point cloud. Conclusively, the experimental of overall scanning strategy show that it can improve target point cloud integrity and density while ensuring the capture of target.

Analysis on Failure Mode Severity of Machining Center Spindle System

According to the subjectivity and fuzziness of analysis on failure mode severity about spindle system of machining center,an analysis model of the failure mode severity of such a system is proposed based on the new fault severity index system, improved analytic hierarchy process( IAHP) and entropy-based fuzzy comprehensive evaluation. IAHP and entropy methods are adopted to determine the comprehensive failure severity index weight. The evaluation result is obtained after the factor set,comment set,weight set,and other parameters are determined,and then the level of risk degree and numerical value order of every spindle system failure mode is given. By taking an example,we verify that the proposed method can quantify the qualitative problem comprehensively,obtain more accurate analysis results,and provide the theoretical reference for mechanization and sequencing of failure mode effect analysis in reliability analysis. The calculation results can also serve as the basis of failure mode,effects,and criticality analysis in the subsequent step.