Intrusion Detection Using Ensemble Wrapper Filter Based Feature Selection with Stacking Model

The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks.For attack detection,the prior system has created an SMSRPF(Stacking Model Significant Rule Power Factor)classifier.To provide creative instance detection,the SMSRPF combines the detection of trained classifiers such as DT(Decision Tree)and RF(Random Forest).Nevertheless,it does not generate any accuratefindings that are adequate.The suggested system has built an EWF(Ensemble Wrapper Filter)feature selection with SMSRPF classifier for attack detection so as to overcome this problem.The UNSW-NB15 dataset is used as an input in this proposed research project.Specifically,min–max normalization approach is used to pre-process the incoming data.The feature selection is then carried out using EWF.Based on the selected features,SMSRPF classifiers are utilized to detect the attacks.The SMSRPF is integrated with the trained classi-fiers such as DT and RF to create creative instance detection.After that,the testing data is classified using MCAR(Multi-Class Classification based on Association Rules).The SRPF judges the rules correctly even when the confidence and the lift measures fail.Regarding accuracy,precision,recall,f-measure,computation time,and error,the experimental findings suggest that the new system outperforms the prior systems.

Emissions of polycyclic aromatic hydrocarbons from coking industries in China

This study set out to assess the characteristics of polycyclic aromatic hydrocarbon （PAH） emission from coking industries, with field samplings conducted at four typical coke plants. For each selected plant, stack flue gas samples were collected during processes that included charging coal into the ovens （CC）, pushing coke （PC） and the combustion of coke-oven gas （CG）. Sixteen individual PAHs on the US EPA priority list were analyzed by gas chromatography/mass spectrometry （GC/MS）. Results showed that the total PAH concentrations in the flue gas ranged from 45.776 to 414.874 μg/m3, with the highest emission level for CC （359.545μg/m3）, The concentration of PAH emitted from the CC process in CP1 （stamp charging） was lower than that from CP3 and CP4 （top charging）. Low-molecular-weight PAHs （i.e., two- to three-ring PAHs） were predominant contributors to the total PAH contents, and Nap, AcPy, Flu, PhA, and AnT were found to be the most abundant ones. Total BaPeq concentrations for CC （2.248 μg/m3） were higher than those for PC （ 1.838 μg/m3 ） and CG （1.082 μg/m3 ）, and DbA was an important contributor to carcinogenic risk as BaP in emissions from coking processes. Particulate PAH accounted for more than 20% of the total BaPeq concentrations, which were significantly higher than the corresponding contributions to the total PAH mass concentration （5%）. Both particulate and gaseous PAH should be taken into consideration when the potential toxicity risk of PAH pollution during coking processes is assessed. The mean total-PAH emission factors were 346.132 and 93.173μg/kg for CC and PC, respectively.