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Conditional Tabular Generative Adversarial Based Intrusion Detection System for Detecting Ddos and Dos Attacks on the
Basim Ahmad Alabsi1, Mohammed Anbar2, Shaza Dawood Ahmed Rihan1
1Applied College, Najran University, Kind Abdulaziz Street, Najran P.O. Box 1988, Saudi Arabia.
Sensors (Basel, Switzerland)
|July 8, 2023
Summary
A new Intrusion Detection System (IDS) uses Conditional Tabular Generative Adversarial Networks (CTGAN) to detect Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks on Internet of Things (IoT) networks, improving detection accuracy.
Area of Science:
- Cybersecurity
- Network Security
- Machine Learning
Background:
- The proliferation of Internet of Things (IoT) devices has escalated the threat landscape, particularly concerning Distributed Denial of Service (DDoS) and Denial of Service (DoS) attacks.
- These attacks pose significant risks, leading to service disruptions and financial repercussions for organizations reliant on IoT infrastructure.
Purpose of the Study:
- To introduce a novel Intrusion Detection System (IDS) specifically designed for identifying DDoS and DoS attacks within IoT environments.
- To leverage Conditional Tabular Generative Adversarial Networks (CTGAN) for enhancing the performance of attack detection models.
Main Methods:
- Development of a CTGAN-based IDS employing a generator to create synthetic traffic data mimicking legitimate patterns and a discriminator to distinguish malicious traffic.
- Utilizing synthetically generated tabular data from CTGAN to train and improve the performance of various shallow machine learning and deep learning classifiers.
- Evaluation of the proposed IDS using the Bot-IoT dataset, assessing key performance metrics such as detection accuracy, precision, recall, and F1 score.
Main Results:
- The proposed CTGAN-based IDS demonstrated accurate detection capabilities for DDoS and DoS attacks in IoT networks.
- Experimental results confirmed the effectiveness of the approach in identifying malicious network traffic.
- The study highlighted the substantial positive impact of CTGAN in enhancing the performance of both machine learning and deep learning classifiers for intrusion detection.
Conclusions:
- The developed CTGAN-based IDS offers a robust solution for detecting DDoS and DoS attacks in IoT networks.
- The integration of CTGAN significantly improves the efficacy of machine learning and deep learning models in identifying cyber threats.
- This approach contributes to bolstering the security and reliability of IoT ecosystems against prevalent network attacks.
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