Zhenxiang He^*, Xunxi Wang, Chunwei Li

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 845-871, 2024, DOI:10.32604/cmc.2023.046607

Abstract In the fast-evolving landscape of digital networks, the incidence of network intrusions has escalated alarmingly. Simultaneously, the crucial role of time series data in intrusion detection remains largely underappreciated, with most systems failing to capture the time-bound nuances of network traffic. This leads to compromised detection accuracy and overlooked temporal patterns. Addressing this gap, we introduce a novel SSAE-TCN-BiLSTM (STL) model that integrates time series analysis, significantly enhancing detection capabilities. Our approach reduces feature dimensionality with a Stacked Sparse Autoencoder (SSAE) and extracts temporally relevant features through a Temporal Convolutional Network (TCN) and Bidirectional Long Short-term Memory Network (Bi-LSTM). By… More >

D. Soares Jr.¹, W.J. Mansur^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.8, No.2, pp. 153-164, 2005, DOI:10.3970/cmes.2005.008.153

Abstract A coupling procedure is described to perform time-domain numerical analyses of dynamic fluid-structure interaction. The fluid sub-domains, where acoustic waves propagate, are modeled by the Boundary Element Method (BEM), which is quite suitable to deal with linear homogeneous unbounded domain problems. The Finite Element Method (FEM), on the other hand, models the structure sub-domains, adopting a time marching scheme based on implicit Green's functions. The BEM/FEM coupling algorithm here developed is very efficient, eliminating the drawbacks of standard and iterative coupling procedures. Stability and accuracy features are improved by the adoption of different time steps in each sub-domain of the… More >