Mitigating distributed denial of service attacks using attribute subset selection with temporal convolutional networks
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces a new Intelligent Framework for Attack Detection Using Salp Swarm-Based Feature Selection and Deep Learning Architecture (IFAD-SSFSDLA) to combat Distributed Denial of Service (DDoS) attacks. The novel model achieves high accuracy in real-time DDoS attack detection.
Area Of Science
- Cybersecurity
- Network Security
- Artificial Intelligence
Background
- Distributed Denial of Service (DDoS) attacks pose a significant and evolving threat to network infrastructure and services.
- Existing methods struggle with the dynamic patterns and complexity of modern DDoS attacks, necessitating advanced detection techniques.
- Real-time identification and mitigation of DDoS threats are crucial to prevent service disruptions and data breaches.
Purpose Of The Study
- To propose a novel Intelligent Framework for Attack Detection Using Salp Swarm-Based Feature Selection and Deep Learning Architecture (IFAD-SSFSDLA) for real-time DDoS attack detection.
- To enhance the accuracy and efficiency of DDoS attack detection through optimized feature selection and deep learning.
- To provide a robust solution for identifying and mitigating the impact of increasingly sophisticated DDoS attacks.
Main Methods
- Data preprocessing using min-max normalization for cleaning and structuring raw network traffic data.
- Feature selection employing the Salp Swarm Algorithm (SSA) to identify and retain the most discriminative features for improved model performance.
- Attack classification utilizing the Temporal Convolutional Network (TCN) deep learning architecture.
Main Results
- The IFAD-SSFSDLA model demonstrated superior performance in detecting DDoS attacks.
- Achieved high accuracy rates of 99.56% on the CIC-IDS-2017 dataset and 99.65% on the Edge-IIoT dataset.
- Outperformed existing techniques in DDoS attack detection accuracy across multiple datasets.
Conclusions
- The proposed IFAD-SSFSDLA model offers an effective and accurate solution for real-time DDoS attack detection.
- The integration of Salp Swarm Algorithm for feature selection and Temporal Convolutional Network for classification significantly enhances detection capabilities.
- This framework provides a valuable advancement in cybersecurity for combating prevalent and evolving network threats.
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