Chin-Shiuh Shieh¹, Thanh-Lam Nguyen¹, Thanh-Tuan Nguyen^2,*, Mong-Fong Horng^1,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4881-4912, 2025, DOI:10.32604/cmc.2025.061001 - 06 March 2025

Abstract DDoS attacks represent one of the most pervasive and evolving threats in cybersecurity, capable of crippling critical infrastructures and disrupting services globally. As networks continue to expand and threats become more sophisticated, there is an urgent need for Intrusion Detection Systems (IDS) capable of handling these challenges effectively. Traditional IDS models frequently have difficulties in detecting new or changing attack patterns since they heavily depend on existing characteristics. This paper presents a novel approach for detecting unknown Distributed Denial of Service (DDoS) attacks by integrating Sliced Iterative Normalizing Flows (SINF) into IDS. SINF utilizes the… More >

Lanyao Zhang¹, Shichao Kan², Yigang Cen³, Xiaoling Chen¹, Linna Zhang^1,*, Yansen Huang^4,5

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1631-1648, 2024, DOI:10.32604/cmc.2024.046924 - 27 February 2024

Abstract Unsupervised methods based on density representation have shown their abilities in anomaly detection, but detection performance still needs to be improved. Specifically, approaches using normalizing flows can accurately evaluate sample distributions, mapping normal features to the normal distribution and anomalous features outside it. Consequently, this paper proposes a Normalizing Flow-based Bidirectional Mapping Residual Network (NF-BMR). It utilizes pre-trained Convolutional Neural Networks (CNN) and normalizing flows to construct discriminative source and target domain feature spaces. Additionally, to better learn feature information in both domain spaces, we propose the Bidirectional Mapping Residual Network (BMR), which maps sample… More > Graphic Abstract