Research Article
Securing the Internet of Things: A Comprehensive Examination of Machine and Deep Learning Approaches Against Denial of Service Attacks
@INPROCEEDINGS{10.1007/978-3-031-81168-5_16, author={Deepak Singh and R. Uma Mageswari}, title={Securing the Internet of Things: A Comprehensive Examination of Machine and Deep Learning Approaches Against Denial of Service Attacks}, proceedings={Broadband Communications, Networks, and Systems. 14th EAI International Conference, BROADNETS 2024, Hyderabad, India, February 16--17, 2024, Proceedings, Part I}, proceedings_a={BROADNETS}, year={2025}, month={2}, keywords={DoS attacks Deep Learning Machine Learning Benchmark-Dataset}, doi={10.1007/978-3-031-81168-5_16} }- Deepak Singh
R. Uma Mageswari
Year: 2025
Securing the Internet of Things: A Comprehensive Examination of Machine and Deep Learning Approaches Against Denial of Service Attacks
BROADNETS
Springer
DOI: 10.1007/978-3-031-81168-5_16
Abstract
The proliferation of Internet of Things (IoT) devices has revolutionized numerous industries, but it has also opened the door to sophisticated cyber threats, particularly Denial of Service (DoS) attacks. This review paper offers a thorough exploration of current methodologies employed in detecting and mitigating DoS attacks within IoT ecosystems, with a primary emphasis on the utilization of machine and deep learning techniques. Through a critical evaluation of the strengths, weaknesses, and limitations inherent in these approaches, this paper aims to identify gaps in existing research and propose innovative directions for future investigations. By addressing these research gaps, we aim to advance the field of DoS attack detection in IoT environments, enhancing the security and resilience of interconnected systems.
