A Study on Dataset Development and Model Vulnerability to Backdoors

• 발행기관: 한국스마트미디어학회
• 저자명: Niringiye Godfrey Dongwoo Kang Hoon-Jae Lee Young Sil Lee
• 간행물 정보: 『스마트미디어저널』제14권 제2호, 67~79쪽, 전체 13쪽
• 주제분류: 공학 > 컴퓨터학
• 파일형태: PDF
• 발행일자: 2025.02.28

국문 초록

Intrusion Detection Systems (IDS) are crucial components designed to detect and prevent unauthorized access to network resources. In this research, we implemented an AI-based IDS through a multifaceted approach that included creating a custom IDS dataset, evaluating it using a Convolutional Neural Network (CNN) model, and analyzing the security and resilience of the CNN model against backdoor attacks. The experimental results demonstrated a significant improvement in the model's accuracy and its resilience to certain types of attacks. However, vulnerabilities to backdoor attacks were still present. Specifically, the successful insertion of hidden triggers into the CNN model during the training phase revealed the model's susceptibility to these types of attacks. These findings emphasize the urgent need for improved strategies to mitigate backdoor attacks in the design and implementation of IDSs.

목차

Ⅰ. INTRODUCTION

Ⅱ. LITERATURE REVIEW

Ⅲ. PROPOSED METHOD

Ⅳ. PERFORMANCE EVALUATION

Ⅴ. CONCLUSION

REFERENCES

키워드

artificial intelligence distributed denial-of-service (DDoS) intrusion detection dataset toolkit (ID2T) IDS CNN backdoor attacks deep Learning