Jun Wang, Changfu Si, Zhen Wang, Qiang Fu^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4297-4318, 2024, DOI:10.32604/cmc.2024.050223

Abstract Nowadays, with the rapid development of industrial Internet technology, on the one hand, advanced industrial control systems (ICS) have improved industrial production efficiency. However, there are more and more cyber-attacks targeting industrial control systems. To ensure the security of industrial networks, intrusion detection systems have been widely used in industrial control systems, and deep neural networks have always been an effective method for identifying cyber attacks. Current intrusion detection methods still suffer from low accuracy and a high false alarm rate. Therefore, it is important to build a more efficient intrusion detection model. This paper… More >

Jing-Doo Wang¹, Chayadi Oktomy Noto Susanto^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1711-1728, 2024, DOI:10.32604/cmes.2024.048955

Abstract A significant obstacle in intelligent transportation systems (ITS) is the capacity to predict traffic flow. Recent advancements in deep neural networks have enabled the development of models to represent traffic flow accurately. However, accurately predicting traffic flow at the individual road level is extremely difficult due to the complex interplay of spatial and temporal factors. This paper proposes a technique for predicting short-term traffic flow data using an architecture that utilizes convolutional bidirectional long short-term memory (Conv-BiLSTM) with attention mechanisms. Prior studies neglected to include data pertaining to factors such as holidays, weather conditions, and More >

Md. Abdur Rahim¹, Fahmid Al Farid², Abu Saleh Musa Miah³, Arpa Kar Puza¹, Md. Nur Alam⁴, Md. Najmul Hossain⁵, Sarina Mansor², Hezerul Abdul Karim^2,6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1689-1710, 2024, DOI:10.32604/cmes.2024.048714

Abstract Handwriting is a unique and significant human feature that distinguishes them from one another. There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification. However, such systems are susceptible to forgery, posing security risks. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures or symbols. In response to these challenges, we propose an innovative hybrid technique for individual identification based on independent handwriting, eliminating the reliance on specific signatures… More >

Aodi Liu, Na Wang^*, Xuehui Du, Dibin Shan, Xiangyu Wu, Wenjuan Wang

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1705-1726, 2024, DOI:10.32604/cmc.2024.049011

Abstract Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access control mechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policy management efficiency and difficulty in accurately describing the access control policy. To overcome these problems, this paper proposes a big data access control mechanism based on a two-layer permission decision structure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes are introduced in the ABAC model as business constraints between entities. The proposed mechanism implements a two-layer permission decision structure composed… More >

Wael M. S. Yafooz^*, Abdullah Alsaeedi

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 735-759, 2024, DOI:10.32604/cmc.2024.048270

Abstract In the last decade, technical advancements and faster Internet speeds have also led to an increasing number of mobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobile apps. The use of these apps eases our daily lives, and all customers who need any type of service can access it easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digital services to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services, particularly during two large occasions,… More >

Wenfeng Zheng¹, Siyu Lu¹, Zhuohang Cai¹, Ruiyang Wang¹, Lei Wang², Lirong Yin^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2729-2745, 2024, DOI:10.32604/cmes.2023.046692

Abstract In the field of natural language processing (NLP), there have been various pre-training language models in recent years, with question answering systems gaining significant attention. However, as algorithms, data, and computing power advance, the issue of increasingly larger models and a growing number of parameters has surfaced. Consequently, model training has become more costly and less efficient. To enhance the efficiency and accuracy of the training process while reducing the model volume, this paper proposes a first-order pruning model PAL-BERT based on the ALBERT model according to the characteristics of question-answering (QA) system and language 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 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… More >

Bauyrzhan Omarov^1,*, Zhuldyz Sailaukyzy², Alfiya Bigaliyeva², Adilzhan Kereyev³, Lyazat Naizabayeva⁴, Aigul Dautbayeva⁵

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3765-3781, 2023, DOI:10.32604/cmc.2023.042469

Abstract In the face of escalating intricacy and heterogeneity within Internet of Things (IoT) network landscapes, the imperative for adept intrusion detection techniques has never been more pressing. This paper delineates a pioneering deep learning-based intrusion detection model: the One Dimensional Convolutional Neural Networks (1D-CNN) and Bidirectional Long Short-Term Memory (BiLSTM) Network (Conv-BiLSTM) augmented with an Attention Mechanism. The primary objective of this research is to engineer a sophisticated model proficient in discerning the nuanced patterns and temporal dependencies quintessential to IoT network traffic data, thereby facilitating the precise categorization of a myriad of intrusion types. Methodology:… More >

Jauzak Hussaini Windiatmaja, Delphi Hanggoro, Muhammad Salman, Riri Fitri Sari^*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2309-2339, 2023, DOI:10.32604/cmc.2023.041152

Abstract This research presents a reputation-based blockchain consensus mechanism called Proof of Intelligent Reputation (PoIR) as an alternative to traditional Proof of Work (PoW). PoIR addresses the limitations of existing reputation-based consensus mechanisms by proposing a more decentralized and fair node selection process. The proposed PoIR consensus combines Bidirectional Long Short-Term Memory (BiLSTM) with the Network Entity Reputation Database (NERD) to generate reputation scores for network entities and select authoritative nodes. NERD records network entity profiles based on various sources, i.e., Warden, Blacklists, DShield, AlienVault Open Threat Exchange (OTX), and MISP (Malware Information Sharing Platform). It… More >