Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks
National University of Computer and Emerging Sciences · Birmingham City University · +2 more institutions
Abstract
The Internet of Things (IoT) is a complex and diverse network consisting of resource-constrained sensors/devices/things that are vulnerable to various security threats, particularly Distributed Denial of Services (DDoS) attacks. Recently, the integration of Software Defined Networking (SDN) with IoT has emerged as a promising approach for improving security and access control mechanisms. However, DDoS attacks continue to pose a significant threat to IoT networks, as they can be executed through botnet or zombie attacks. Machine learning-based security frameworks offer a viable solution to scrutinize the behavior of IoT devices and compile a profile that enables the decision-making process to maintain the…
Citation impact
- FWCI
- 39.93
- Percentile
- 100%
- References
- 95
Authors
6- JBJalal Bhayo
National University of Computer and Emerging Sciences
- SASyed Attique ShahCorresponding
Birmingham City University
- SHSufian Hameed
National University of Computer and Emerging Sciences
- AAAwais Ahmed
University of Electronic Science and Technology of China
- JAJamal Abdul Nasir
National University of Computer and Emerging Sciences
Topics & keywords
- Computer science
- Denial-of-service attack
- Testbed
- Software-defined networking
- Machine learning
- Artificial intelligence
- Application layer DDoS attack
- Controller (irrigation)