Network Meddling Detection Using Machine Learning Empowered with Blockchain Technology

【Author】 Nasir, Muhammad Umar; Khan, Safiullah; Mehmood, Shahid; Khan, Muhammad Adnan; Zubair, Muhammad; Hwang, Seong Oun

【Source】SENSORS

【影响因子】3.847

【Abstract】The study presents a framework to analyze and detect meddling in real-time network data and identify numerous meddling patterns that may be harmful to various communication means, academic institutes, and other industries. The major challenge was to develop a non-faulty framework to detect meddling (to overcome the traditional ways). With the development of machine learning technology, detecting and stopping the meddling process in the early stages is much easier. In this study, the proposed framework uses numerous data collection and processing techniques and machine learning techniques to train the meddling data and detect anomalies. The proposed framework uses support vector machine (SVM) and K-nearest neighbor (KNN) machine learning algorithms to detect the meddling in a network entangled with blockchain technology to ensure the privacy and protection of models as well as communication data. SVM achieves the highest training detection accuracy (DA) and misclassification rate (MCR) of 99.59% and 0.41%, respectively, and SVM achieves the highest-testing DA and MCR of 99.05% and 0.95%, respectively. The presented framework portrays the best meddling detection results, which are very helpful for various communication and transaction processes.

【Keywords】machine learning; network security; meddling detection; cyber attack

【发表时间】2022 SEP

【收录时间】2022-10-11

【文献类型】实证数据

【主题类别】

区块链技术-协同技术-机器学习