인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Network intrusion detection systems (NIDSs) are one of the main tools used to defend against cyber-attacks. Deep learning has shown remarkable success in network intrusion detection. However, the effect of feature fusion has yet to be explored in how to boost the performance of the deep learning model and improve its generalisation capability in NIDS. In this paper, we propose novel deep learning architectures with different feature fusion mechanisms aimed at improving the performance of the multi-classification components of NIDS. We propose three different deep learning models, which we call early-fusion, late-fusion, and late-ensemble learning models using feature fusion with fully connected deep networks. Our feature fusion mechanisms were designed to encourage deep learning models to learn relationships between different input features more efficiently and mitigate any potential bias that may occur with a particular feature type. To assess the efficacy of our deep learning solutions and make comparisons with state-of-the-art models, we employ the widely accessible UNSW-NB15 and NSL-KDD datasets specifically designed to enhance the development and evaluation of improved NIDSs. Through quantitative analysis, we demonstrate the resilience of our proposed models in effectively addressing the challenges posed by multi-classification tasks, especially in the presence of class imbalance issues. Moreover, our late-fusion and late-ensemble models showed the best generalisation behaviour (against overfitting) with similar performance on the training and validation sets.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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