Applying machine learning enabled myriad fragment empirical modes in 5G communications to detect profile injection attacks

• Published in: Wireless networks Vol. 30; no. 6; pp. 5533 - 5546
• Main Authors: Alzaidi, Mohammed S., Shukla, Piyush Kumar, Sangeetha, V., Pandagre, Karuna Nidhi, Minchula, Vinodh Kumar, Sharma, Sachin, Khan, Arfat Ahmad, Prashanth, V.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2024; Springer Nature B.V
• Subjects: Assaults; Black holes; Communications Engineering; Computer Communication Networks; Damage; Denial of service attacks; Eavesdropping; Electrical Engineering; Engineering; IT in Business; Jamming; Machine learning; Mobile ad hoc networks; Network topologies; Networks; Nodes; Packets (communication); Routing (telecommunications); Traffic analysis; Wireless communications; Wireless networks; Wormhole routeing; 5G communication; Feature extraction; Improved support vector classification; Profile injection attack detection

In order to facilitate communication, wireless networks are built from a collection of nodes that may be either static or dynamic. They are acquiring a lot of popularity in the area of research due to the fact that they are ad hoc in nature, and the number of users of mobile devices is rising day by...