Detecting Web Attacks Based on Clustering Algorithm and Multi-branch CNN

CSKH-02.2020. Abstract—This paper proposes and develops a web attack detection model that combines a clustering algorithm and a multi-branch convolutional neural network (CNN). The original feature set was clustered into clusters of similar features. Each cluster of similar features was generalized in a convolutional structure of a branch of the CNN. The component feature vectors are assembled into a synthetic feature vector and included in a fully connected layer for classification. Using K-fold cross-validation, the accuracy of the proposed method 98.8%, F1-score is 98.9% and the improvement rate of accuracy is 1.479%.

Tóm tắt—Bài báo đề xuất và phát triển mô hình phát hiện tấn công Web dựa trên kết hợp thuật toán phân cụm và mạng nơ-ron tích chập (CNN) đa nhánh. Tập đặc trưng ban đầu được phân cụm thành các nhóm đặc trưng tương ứng. Mỗi nhóm đặc trưng được khái quát hoá trong một nhánh của mạng CNN đa nhánh để tạo thành một vector đặc trưng thành phần. Các vector đặc trưng thành phần được ghép lại thành một vector đặc trưng tổng hợp và đưa vào lớp liên kết đầy đủ để phân lớp. Sử dụng phương pháp kiểm thử chéo trên mô hình đề xuất, độ chính xác đạt 98,8%, F1-score đạt 98,8% và tỉ lệ cải tiến độ chính xác là 1,479%.

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Thông tin trích dẫn: Pham Van Huong, Le Thi Hong Van, Pham Sy Nguyen, “Detecting Web Attacks Based on Clustering Algorithm and Multi-branch CNN”, Journal of Science and Technology on Information Security, ISSN 2615-9570, Vol. 12, No. 02, 2020, pp. 31-37.