Blockchain enhanced distributed denial of service detection in IoT using deep learning and evolutionary computation
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a novel Metaheuristic-Optimized Blockchain Framework for Attack Detection using a Deep Learning Model (MOBCF-ADDLM) to combat Distributed Denial of Service (DDoS) threats in Internet of Things (IoT) environments. The proposed method achieves 99.22% accuracy, significantly enhancing IoT security.
Area Of Science
- Cybersecurity
- Network Security
- Artificial Intelligence
Background
- The Internet of Things (IoT) faces significant security challenges, particularly Distributed Denial of Service (DDoS) attacks, due to resource-constrained devices.
- Blockchain technology (BC) offers potential decentralized security solutions for IoT vulnerabilities.
- Existing methods struggle to effectively detect and mitigate DDoS threats in complex IoT ecosystems.
Purpose Of The Study
- To propose an effective method for detecting DDoS threats in IoT environments using advanced techniques.
- To enhance the security of IoT applications by leveraging Blockchain and Deep Learning.
- To introduce the Metaheuristic-Optimized Blockchain Framework for Attack Detection using a Deep Learning Model (MOBCF-ADDLM).
Main Methods
- Utilizing Blockchain technology for decentralized security solutions against DDoS attacks.
- Implementing min-max scaling for data preprocessing and the Aquila Optimizer (AO) for feature selection (FS).
- Employing a Deep Belief Network (DBN) for attack classification, with hyper-parameter optimization by the Red Panda Optimizer (RPO).
Main Results
- The MOBCF-ADDLM approach demonstrated superior performance in detecting DDoS threats.
- Experiments conducted on BoT-IoT Binary and Multiclass datasets validated the model's effectiveness.
- The proposed method achieved a high accuracy of 99.22%, outperforming existing models.
Conclusions
- The MOBCF-ADDLM framework provides an effective and accurate solution for DDoS attack detection in IoT systems.
- The integration of Blockchain, Deep Learning, and metaheuristic optimization significantly enhances IoT security.
- This research contributes a robust defense mechanism against prevalent cyber threats in the expanding IoT landscape.
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