Detection of DDoS Cyberattack Using a Hybrid Trust-Based Technique for Smart Home Networks

As Smart Home Internet of Things (SHIoT) continue to evolve, improving connectivity and security whilst offering convenience, ease, and efficiency is crucial. SHIoT networks are vulnerable to several cyberattacks, including Distributed Denial of Service (DDoS) attacks. The ever-changing landscape of Smart Home IoT threats presents many problems for current cybersecurity techniques. In response, we propose a hybrid Trust-based approach for DDoS attack detection and mitigation. Our proposed technique incorporates adaptive mechanisms and trust evaluation models to monitor device behaviour and identify malicious nodes dynamically. By leveraging real-time threat detection and secure routing protocols, the proposed trust-based mechanism ensures uninterrupted communication and minimizes the attack surface. Additionally, energy-efficient techniques are employed to safeguard communication without overburdening resource-constrained SHIoT devices. To evaluate the effectiveness of the proposed technique in efficiently detecting and mitigating DDoS attacks, we conducted several simulation experiments and compared the performance of the approach with other existing DDoS detection mechanisms. The results showed notable improvements in terms of energy efficiency, improved system resilience and enhanced computations. Our solution offers a targeted approach to securing Smart Home IoT environments against evolving cyber threats.

@article{Okporokpo2025,
  title     = {Detection of DDoS Cyberattack Using a Hybrid Trust-Based Technique for Smart Home Networks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {1},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Oghenetejiri Okporokpo and Funminiyi Olajide and Nemitari Ajienka and Xiaoqi Ma},
  doi       = {10.14569/IJACSA.2025.0160103},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160103}
}