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An Efficient Stabbing Based Intrusion Detection Framework for Sensor Networks

Computer Systems Science & Engineering 2022年第10期43卷 141-157页

作者： a.arivazhagi S.Raja Kumar Department of Computer Science and Engineering University College of EngineeringAriyalurTamilnaduIndia Department of Mathematics University College of EngineeringAriyalurTamilnaduIndia

Intelligent Intrusion Detection System(IIDS)for networks provide a resourceful solution to network security than conventional intrusion defence mechanisms like a *** efficiency of IIDS highly relies on the algorithm *** enhancements towards these methods are utilized to enhance the classification accuracy and diminish the testing and training time of these ***,a novel and intelligent learning approach are known as the stabbing of intrusion with learning framework(SILF),is proposed to learn the attack features and reduce the *** also reduces the testing and training time effectively and enhances Linear Support Vector Machine(l-SVM).It constructs an auto-encoder method,an efficient learning approach for feature construction unsupervised ***,the inclusive certified signature(ICS)is added to the encoder and decoder to preserve the sensitive data without being harmed by the *** training the samples in the preliminary stage,the selected features are provided into the classifier(lSVM)to enhance the prediction ability for intrusion and classification ***,the model efficiency is learned *** multi-classification is examined and compared with various classifier approaches like conventional SVM,Random Forest(RF),Recurrent Neural Network(RNN),STL-IDS and game *** outcomes show that the proposed l-SVM has triggered the prediction rate by effectual testing and training and proves that the model is more efficient than the traditional approaches in terms of performance metrics like accuracy,precision,recall,F-measure,pvalue,MCC and so *** proposed SILF enhances network intrusion detection and offers a novel research methodology for intrusion ***,the simulation is done with a MATLAB environment where the proposed model shows a better trade-off compared to prevailing approaches.

关键词： Network security sensor network intrusion detection learning framework linear support vector machine the detection mechanism

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