Abstract
The Internet of Things (IoT) is a decentralized and ever-changing network, which poses challenges in terms of security. The input highlights the need for robust security measures to protect IoT devices and their data from potential threats. The study focuses on Federated Learning (FL) technology as a potential solution to enhance IoT security. FL models are designed to protect sensitive data while allowing its exchange with other systems, making it a promising approach for securing IoT environments. Additionally, the input suggests the implementation of intrusion detection systems (IDS) as an additional strategy to enhance overall IoT security. By combining FL and IDS, the aim is to develop a comprehensive solution to address the complex problem of protecting IoT settings. The input emphasizes the significance of exploring machine learning (ML) techniques to improve security protocols for IoT devices. It also highlights the importance of validating the effectiveness of FL technology in safeguarding and transferring confidential information within IoT systems. The integration of IDS is proposed as an extra measure to strengthen the security of IoT systems as a whole. Ultimately, the objective of this research is to provide comprehensive and effective solutions to address security challenges in the IoT, thereby increasing trust in the application of this technology across various domains.
Recommended Citation
Mohammed, Mohammed Q.; Alrahman, Zena Abd; and Shehab, Aouf R.
(2024)
"Investigating Intrusion Detection System Using Federated Learning for IoT Security Challenges,"
Iraqi Journal for Computer Science and Mathematics: Vol. 5:
Iss.
4, Article 12.
DOI: https://doi.org/10.52866/2788-7421.1218
Available at:
https://ijcsm.researchcommons.org/ijcsm/vol5/iss4/12
