SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)
Abstract 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 re...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-09-01
|
| Series: | CAAI Transactions on Intelligence Technology |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/cit2.12003 |
| _version_ | 1811250407332118528 |
|---|---|
| author | Azka Wani Revathi S Rubeena Khaliq |
| author_facet | Azka Wani Revathi S Rubeena Khaliq |
| author_sort | Azka Wani |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-12T16:04:14Z |
| format | Article |
| id | doaj.art-36d20c050d7c46288d223ab78ca016cb |
| institution | Directory Open Access Journal |
| issn | 2468-2322 |
| language | English |
| last_indexed | 2024-04-12T16:04:14Z |
| publishDate | 2021-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | CAAI Transactions on Intelligence Technology |
| spelling | doaj.art-36d20c050d7c46288d223ab78ca016cb2022-12-22T03:26:08ZengWileyCAAI Transactions on Intelligence Technology2468-23222021-09-016328129010.1049/cit2.12003SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL)Azka Wani0Revathi S1Rubeena Khaliq2Department of Computer Applications Crescent B S Abdur Rahman Institute of Science and Technology Chennai IndiaDepartment of Computer Science and Engineering Crescent B S Abdur Rahman Institute of Science and Technology Chennai IndiaDepartment of Mathematics Crescent B S Abdur Rahman Institute of Science and Technology Chennai IndiaAbstract 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.https://doi.org/10.1049/cit2.12003Internet of Thingssoftware defined networkingprotocolscomputer network securitydeep learning (artificial intelligence)telecommunication computing |
| spellingShingle | Azka Wani Revathi S Rubeena Khaliq SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL) CAAI Transactions on Intelligence Technology Internet of Things software defined networking protocols computer network security deep learning (artificial intelligence) telecommunication computing |
| title | SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL) |
| title_full | SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL) |
| title_fullStr | SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL) |
| title_full_unstemmed | SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL) |
| title_short | SDN‐based intrusion detection system for IoT using deep learning classifier (IDSIoT‐SDL) |
| title_sort | sdn based intrusion detection system for iot using deep learning classifier idsiot sdl |
| topic | Internet of Things software defined networking protocols computer network security deep learning (artificial intelligence) telecommunication computing |
| url | https://doi.org/10.1049/cit2.12003 |
| work_keys_str_mv | AT azkawani sdnbasedintrusiondetectionsystemforiotusingdeeplearningclassifieridsiotsdl AT revathis sdnbasedintrusiondetectionsystemforiotusingdeeplearningclassifieridsiotsdl AT rubeenakhaliq sdnbasedintrusiondetectionsystemforiotusingdeeplearningclassifieridsiotsdl |