악성 URL 탐지를 위한 URL Lexical Feature 기반의 DL-ML Fusion Hybrid 모델
DL-ML Fusion Hybrid Model for Malicious Web Site URL Detection Based on URL Lexical Features
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초록
최근에는 인공지능을 활용하여 악성 URL을 탐지하는 다양한 연구가 진행되고 있으며, 대부분의 연구 결과에서 높은 탐지 성능을 보였다. 그러나 고전 머신러닝을 활용하는 경우 feature를 분석하고 선별해야 하는 추가 비용이 발생하며, 데이터 분석가의 역량에 따라 탐지 성능이 결정되는 이슈가 있다. 본 논문에서는 이러한 이슈를 해결하기 위해 URL lexical feature를 자동으로 추출하는 딥러닝 모델의 일부가 고전 머신러닝 모델에 결합된 형태인 DL-ML Fusion Hybrid 모델을 제안한다. 제안한 모델로 직접 수집한 총 6만 개의 악성과 정상 URL을 학습한 결과 탐지 성능이 최대 23.98%p 향상되었을 뿐만 아니라, 자동화된 feature engineering을 통해 효율적인 기계학습이 가능하였다.
Abstract
Recently, various studies on malicious URL detection using artificial intelligence have been conducted, and most of the research have shown great detection performance. However, not only does classical machine learning require a process of analyzing features, but the detection performance of a trained model also depends on the data analyst's ability. In this paper, we propose a DL-ML Fusion Hybrid Model for malicious web site URL detection based on URL lexical features. the propose model combines the automatic feature extraction layer of deep learning and classical machine learning to improve the feature engineering issue. 60,000 malicious and normal URLs were collected for the experiment and the results showed 23.98%p performance improvement in maximum. In addition, it was possible to train a model in an efficient way with the automation of feature engineering.
참고문헌 (16)
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