| Title | A Comparative Study on Machine Learning based Cross Layer Security in Internet of Things (IoT) |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Saranya, K., Valarmathi, Dr. A. |
| Conference Name | 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS) |
| Keywords | composability, compositionality, Computer architecture, Cross layer design, Cross Layer Security, Energy efficiency, Internet of Things, interoperability, machine learning, privacy, Protocols, pubcrawl, renewable energy sources, resilience, Resiliency, Scalability, security, wireless networks |
| Abstract | The Internet of Things is a developing technology that converts physical objects into virtual objects connected to the internet using wired and wireless network architecture. Use of cross-layer techniques in the internet of things is primarily driven by the high heterogeneity of hardware and software capabilities. Although traditional layered architecture has been effective for a while, cross-layer protocols have the potential to greatly improve a number of wireless network characteristics, including bandwidth and energy usage. Also, one of the main concerns with the internet of things is security, and machine learning (ML) techniques are thought to be the most cuttingedge and viable approach. This has led to a plethora of new research directions for tackling IoT's growing security issues. In the proposed study, a number of cross-layer approaches based on machine learning techniques that have been offered in the past to address issues and challenges brought on by the variety of IoT are in-depth examined. Additionally, the main issues are mentioned and analyzed, including those related to scalability, interoperability, security, privacy, mobility, and energy utilization. |
| DOI | 10.1109/ICACRS55517.2022.10029035 |
| Citation Key | saranya_comparative_2022 |
A Comparative Study on Machine Learning based Cross Layer Security in Internet of Things (IoT)