Open AccessSDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)
Author(s) -
Wani Azka,
S Revathi,
Khaliq Rubeena
Publication year - 2021
Publication title -
caai transactions on intelligence technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.613
H-Index - 15
ISSN - 2468-2322
DOI - 10.1049/cit2.12003
Subject(s) - computer science , intrusion detection system , internet of things , fog computing , protocol (science) , classifier (uml) , computer security , embedded system , artificial intelligence , medicine , alternative medicine , pathology
The participation of ordinary devices in networking has created a world of connected devices rapidly. The Internet of Things (IoT) includes heterogeneous devices from every field. There are no definite protocols or standards for IoT communication, and most of the IoT devices have limited resources. Enabling a complete security measure for such devices is a challenging task, yet necessary. Many lightweight security solutions have surfaced lately for IoT. The lightweight security protocols are unable to provide an optimum protection against prevailing powerful threats in cyber world. It is also hard to deploy any traditional security protocol on resource‐constrained IoT devices. Software‐defined networking introduces a centralized control in computer networks. SDN has a programmable approach towards networking that decouples control and data planes. An SDN‐based intrusion detection system is proposed which uses deep learning classifier for detection of anomalies in IoT. The proposed intrusion detection system does not burden the IoT devices with security profiles. The proposed work is executed on the simulated environment. The results of the simulation test are evaluated using various matrices and compared with other relevant methods.