Intrusion Detection System Using FKNN and Improved PSO

作     者：Raniyah Wazirali 

作者机构：College of Computing and InformaticsSaudi Electronic UniversitySaudi ArabiaRiyadh 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第67卷第5期

页      面：1429-1445页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：FKNN PSO approach machine learning-based cybersecurity intrusion detection 

摘      要：Intrusion detection system(IDS)techniques are used in cybersecurity to protect and safeguard sensitive *** increasing network security risks can be mitigated by implementing effective IDS methods as a defense *** proposed research presents an IDS model based on the methodology of the adaptive fuzzy k-nearest neighbor(FKNN)*** this method,two parameters,i.e.,the neighborhood size(k)and fuzzy strength parameter(m)were characterized by implementing the particle swarm optimization(PSO).In addition to being used for FKNN parametric optimization,PSO is also used for selecting the conditional feature subsets for *** proficiently regulate the indigenous and comprehensive search skill of the PSO approach,two control parameters containing the time-varying inertia weight(TVIW)and time-varying acceleration coefficients(TVAC)were applied to the *** addition,continuous and binary PSO algorithms were both executed on a multi-core *** proposed IDS model was compared with other state-of-the-art *** results of the proposed methodology are superior to the rest of the techniques in terms of the classification accuracy,precision,recall,and *** results showed that the proposed methods gave the highest performance scores compared to the other conventional algorithms in detecting all the attack types in two ***,the proposed method was able to obtain a large number of true positives and negatives,with minimal number of false positives and negatives.