A Hybrid Machine Learning Framework for Security Intrusion Detection

作     者：Fatimah Mudhhi Alanazi Bothina Abdelmeneem Elsobky Shaimaa Aly Elmorsy 

作者机构：Mathematics and Computer Science DepartmentFaculty of ScienceAlexandria UniversityAlexandriaEgypt 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2024年第48卷第3期

页      面：835-851页

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Cybersecurity fuzzy sets classification internet of things 

摘      要：Proliferation of technology,coupled with networking growth,has catapulted cybersecurity to the forefront of modern security *** this landscape,the precise detection of cyberattacks and anomalies within networks is crucial,necessitating the development of efficient intrusion detection systems(IDS).This article introduces a framework utilizing the fusion of fuzzy sets with support vector machines(SVM),named *** core strategy of FSVM lies in calculating the significance of network features to determine their relative *** with minimal significance are prudently disregarded,a method akin to feature *** process not only curtails the computational burden of the classification algorithm but also ensures the preservation of high accuracy *** ascertain the efficacy of the FSVM model,we have employed a publicly available dataset from Kaggle,which encompasses two distinct decision *** evaluation methodology involves a comprehensive comparison of the classification accuracy of the processed dataset against four contemporary models in the *** performance metrics scores are meticulously calculated for each *** comparative analysis reveals that the FSVM model demonstrates a marked superiority over its counterparts,enhancing classification accuracy by a minimum of 3%.These findings underscore the FSVM model’s robustness and reliability,positioning it as a highly effective tool in the realm of cybersecurity.