Yizhao Wang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3029-3045, 2024, DOI:10.32604/cmes.2024.046270

Abstract Near-fault impulsive ground-shaking is highly destructive to engineering structures, so its accurate identification ground-shaking is a top priority in the engineering field. However, due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods, the generalization and accuracy of the identification process are low. To address these problems, an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed. Firstly, ground-shaking characteristics were analyzed and ground-shaking the data was annotated using Baker’s method. Secondly, the Principal Component Analysis (PCA) method was used to extract the most relevant features related to impulsive ground-shaking. Thirdly, a… More >

Fatma Harby¹, Mansor Alohali², Adel Thaljaoui^2,3,*, Amira Samy Talaat⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2689-2719, 2024, DOI:10.32604/cmc.2024.046623

Abstract Machine Learning (ML) algorithms play a pivotal role in Speech Emotion Recognition (SER), although they encounter a formidable obstacle in accurately discerning a speaker’s emotional state. The examination of the emotional states of speakers holds significant importance in a range of real-time applications, including but not limited to virtual reality, human-robot interaction, emergency centers, and human behavior assessment. Accurately identifying emotions in the SER process relies on extracting relevant information from audio inputs. Previous studies on SER have predominantly utilized short-time characteristics such as Mel Frequency Cepstral Coefficients (MFCCs) due to their ability to capture the periodic nature of audio… More >

Bayi Xu¹, Lei Sun^2,*, Xiuqing Mao², Chengwei Liu³, Zhiyi Ding²

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1995-2022, 2024, DOI:10.32604/cmc.2023.046478

Abstract In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models, we apply various preprocessing… More >

Jian Pang^1,3, Tingting Mao², Wenyu Jia³, Xiaoli Jia^3,*, Peisong Dai², Haibo Huang^1,2,*

Sound & Vibration, Vol.58, pp. 59-80, 2024, DOI:10.32604/sv.2024.046247

Abstract Currently, the inexorable trend toward the electrification of automobiles has heightened the prominence of road noise within overall vehicle noise. Consequently, an in-depth investigation into automobile road noise holds substantial practical importance. Previous research endeavors have predominantly centered on the formulation of mechanism models and data-driven models. While mechanism models offer robust controllability, their application encounters challenges in intricate analyses of vehicle body acoustic-vibration coupling, and the effective utilization of accumulated data remains elusive. In contrast, data-driven models exhibit efficient modeling capabilities and can assimilate conceptual vehicle knowledge, but they impose stringent requirements on both data quality and quantity. In… More >

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 >

Azar Abid Salih^1,*, Maiwan Bahjat Abdulrazaq²

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1275-1295, 2024, DOI:10.32604/cmc.2023.046101

Abstract Cyberspace is extremely dynamic, with new attacks arising daily. Protecting cybersecurity controls is vital for network security. Deep Learning (DL) models find widespread use across various fields, with cybersecurity being one of the most crucial due to their rapid cyberattack detection capabilities on networks and hosts. The capabilities of DL in feature learning and analyzing extensive data volumes lead to the recognition of network traffic patterns. This study presents novel lightweight DL models, known as Cybernet models, for the detection and recognition of various cyber Distributed Denial of Service (DDoS) attacks. These models were constructed to have a reasonable number… More >

Haizhen Wang^1,2,*, Jinying Yan^1,*, Na Jia¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 569-588, 2024, DOI:10.32604/cmc.2023.046055

Abstract Encrypted traffic identification pertains to the precise acquisition and categorization of data from traffic datasets containing imbalanced and obscured content. The extraction of encrypted traffic attributes and their subsequent identification presents a formidable challenge. The existing models have predominantly relied on direct extraction of encrypted traffic data from imbalanced datasets, with the dataset’s imbalance significantly affecting the model’s performance. In the present study, a new model, referred to as UD-VLD (Unbalanced Dataset-VAE-LSTM-DRN), was proposed to address above problem. The proposed model is an encrypted traffic identification model for handling unbalanced datasets. The encoder of the variational autoencoder (VAE) is combined… More >

Xuecheng Yu¹, Yan Huang², Yu Zhang¹, Mingyang Song¹, Zhenhong Jia^1,3,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 473-492, 2024, DOI:10.32604/cmc.2023.044999

Abstract With the increasing dimensionality of network traffic, extracting effective traffic features and improving the identification accuracy of different intrusion traffic have become critical in intrusion detection systems (IDS). However, both unsupervised and semisupervised anomalous traffic detection methods suffer from the drawback of ignoring potential correlations between features, resulting in an analysis that is not an optimal set. Therefore, in order to extract more representative traffic features as well as to improve the accuracy of traffic identification, this paper proposes a feature dimensionality reduction method combining principal component analysis and Hotelling’s T² and a multilayer convolutional bidirectional long short-term memory (MSC_BiLSTM)… More >

Qing Ai^1,2, Hao Tian^2,3,*, Hui Wang^1,*, Qing Lang¹, Xingchun Huang¹, Xinghong Jiang⁴, Qiang Jing⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1797-1827, 2024, DOI:10.32604/cmes.2023.045251

Abstract Structural Health Monitoring (SHM) systems have become a crucial tool for the operational management of long tunnels. For immersed tunnels exposed to both traffic loads and the effects of the marine environment, efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge. This study proposed a model-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel. Firstly, a dynamic predictive model-based anomaly detection method is proposed, which utilizes a rolling time window for modeling to achieve dynamic prediction. Leveraging the assumption… More >

Manjur Kolhar^*, Sultan Mesfer Aldossary

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2857-2879, 2023, DOI:10.32604/cmc.2023.042726

Abstract In many commercial and public sectors, the Internet of Things (IoT) is deeply embedded. Cyber security threats aimed at compromising the security, reliability, or accessibility of data are a serious concern for the IoT. Due to the collection of data from several IoT devices, the IoT presents unique challenges for detecting anomalous behavior. It is the responsibility of an Intrusion Detection System (IDS) to ensure the security of a network by reporting any suspicious activity. By identifying failed and successful attacks, IDS provides a more comprehensive security capability. A reliable and efficient anomaly detection system is essential for IoT-driven decision-making.… More >