Earticle

초록

영어

As cyber threats continue to evolve in sophistication, there is urgent need for intelligent, adaptive and context aware intrusion detection systems. In this paper, we present an intrusion detection framework that employs deep learning models to detect anomalies in network traffic using custom dataset. The dataset was constructed in a controlled lab environment using various intrusion attack scenarios such as DoS, SSH abuse and VPN exploitation. Deep learning models were then applied to detect the intrusions. The performance of the models in performing detection tasks were evaluated using metrics of accuracy, precision, recall and F1- score. The results that were obtained indicate that hybrid model achieved the best results with overall accuracy of 0.99 followed by transformer (0.98) and LSTM model (0.97) being the last. This study highlights the potential of leveraging well designed custom IDS datasets and deep learning techniques to enhance intrusion detection mechanisms thereby providing a robust framework for intrusion detection applications.

목차

Abstract
1. Introduction
2. Background and Motivation
3. Proposed Method
3.1 IDS Raw sample collection.
3.2 Feature extraction
3.3 Data preprocessing
4. IDS AI model construction
5. IDS AI Model Evaluation
6. Results and Analysis
7. Conclusion
Acknowledgement
References

키워드

저자

• Niringiye Godfrey [ Department of Computer Engineering, Dongseo University ]
• Hoon Jae Lee [ Professor, Dept. of Information Security, Dongseo University ] Corresponding Author
• Suk-Ho Lee [ Professor, Dept. of Information Security, Dongseo University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  The International Journal of Advanced Smart Convergence

• 간기

  계간

• pISSN

  2288-2847

• eISSN

  2288-2855

• 수록기간

  2012~2025

• 십진분류

  KDC 326 DDC 380