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Situation-adaptive neural network for fast pre-computing image enhancement

Science China(Information Sciences) 2025年第2期68卷 380-382页

作者： Xinyue LI Huiyu DUAN Jia WANG Xiaohong LIU Yitong CHEN Guangtao ZHAI Institute of Image Communication and Network Engineering Department of Electronic EngineeringShanghai Jiao Tong University

As intelligent vision tasks become more widespread, enhancing image quality before further computational analysis is crucial. Recently, deep learning has shown potential for automated pre-computing enhancement, but it typically requires substantial computational resources and is hard to adapt to in multiple situations without *** practice, image enhancement often demands flexible adjustments based on different situations and subsequent computation devices, such as optical computing.

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Blockchain-Based Certificateless Cross-Domain Authentication Scheme in the Industrial Internet of Things

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computers, Materials & Continua 2024年第9期80卷 3835-3854页

作者： Zhaobin Li Xiantao Liu Nan Zhang Zhanzhen Wei Department of Electronic and Communication Engineering Beijing Electronic Science and Technology InstituteBeijing100070China

The Industrial Internet of Things(IIoT)consists of massive devices in different management domains,and the lack of trust among cross-domain entities leads to risks of data security and privacy leakage during information *** address the above challenges,a viable solution that combines Certificateless Public Key Cryptography(CL-PKC)with blockchain technology can be ***,as many existing schemes rely on a single Key Generation Center(KGC),they are prone to problems such as single points of failure and high computational *** this case,this paper proposes a novel blockchain-based certificateless cross-domain authentication scheme,that integrates the threshold secret sharing mechanism without a trusted center,meanwhile,adopts blockchain technology to enable cross-domain entities to authenticate with each other and to negotiate session keys *** scheme also supports the dynamic joining and removing of multiple KGCs,ensuring secure and efficient cross-domain authentication and key *** analysiswith other protocols demonstrates that the proposed cross-domain authentication protocol can achieve high security with relatively lowcomputational ***,this paper evaluates the scheme based on Hyperledger Fabric blockchain environment and simulates the performance of the certificateless scheme under different threshold parameters,and the simulation results show that the scheme has high performance.

关键词： IIoT blockchain certificateless cross-domain authentication

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WAS:improved white-box cryptographic algorithm over AS iteration

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Cybersecurity 2025年第1期8卷 142-152页

作者： Yatao Yang Yuying Zhai Hui Dong Yanshuo Zhang Department of Electronic and Communication Engineering Beijing Electronic Science and Technology InstituteBeijing 100070China School of Telecommunication Engineering Xidian UniversityXi’an 710071China

The attacker in white-box model has full access to software implementation of a cryptographic algorithm and full control over its execution *** order to solve the issues of high storage cost and inadequate security about most current white-box cryptographic schemes,WAS,an improved white-box cryptographic algorithm over AS iteration is *** scheme utilizes the AS iterative structure to construct a lookup table with a fvelayer ASASA structure,and the maximum distance separable matrix is used as a linear layer to achieve complete difusion in a small number of *** can be prevented from recovering the key under black-box *** length of nonlinear layer S and afne layer A in lookup table is 16 bits,which efectively avoids decomposition attack against the ASASA structure and makes the algorithm possess anti-key extraction security under the whitebox model,while WAS possesses weak white-box(32 KB,112)-space hardness to satisfy anti-code lifting *** has provable security and better storage cost than existing schemes,with the same anti-key extraction security and anti-code lifting security,only 128 KB of memory space is required in WAS,which is only 14%of SPACE-16 algorithm and 33%of Yoroi-16 algorithm.

关键词： White-box cryptography Block cipher Substitution permutation network structure Anti-key extraction Anti-code lifting

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Intelligent integrated sensing and communication:a survey

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Science China(Information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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VoWi-Fi security threats: Address resolution protocol attack and countermeasures

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IET Networks 2024年第2期13卷 129-146页

作者： Lu, Kuan-Chu Liu, I.-Hsien Chang, Keng-Hao Li, Jung-Shian Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan

B5G/6G networks are facing challenges in the deployment of additional base stations. However, Taiwan's four major operators have launched VoWi-Fi calling services to maintain signal quality and coverage for customers. These services pose potential threats when users connect to untrusted Wi-Fi networks. Therefore, the authors utilised commercial equipment to study the security of VoWi-Fi calling services offered by Taiwan's four major telecom companies. The authors employed address resolution protocol attack methods to develop two verification attacks that bypass existing security measures: one for dropping session initiation protocol packets and the other for dropping voice call packets, both capable of circumventing current security defences. Through real-world experiments, the authors confirmed their feasibility and assessed their potential harm. Consequently, two defence methods are proposed. The first is an anti-attack algorithm for app and device manufacturers to detect the security of the user's calling environment. The second is a recommendation for telecom operators to implement new detection mechanisms to safeguard user rights. The cover image is based on the Case Study VoWi-Fi security threats: Address resolution protocol attack and countermeasures by Kuan-Chu Lu et al., https://***/10.1049/ntw2.12113. © 2024 The Authors. IET Networks published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Wi-Fi

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Common criteria for security evaluation and malicious intrusion detection mechanism of dam supervisory control and data acquisition system

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IET Networks 2024年第5-6期13卷 546-559页

作者： Lu, Kuan-Chu Liu, I-Hsien Liu, Zong-Chao Li, Jung-Shian Department of Electrical Engineering Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan

Supervisory control and data acquisition (SCADA) systems are vital in monitoring and controlling industrial processes through the web. However, while such systems result in lower costs, greater utilisation efficiency, and improved reliability, they are vulnerable to cyberattacks, with consequences ranging from the inconvenience and minor disruption to severe physical damage and even loss of life. The authors evaluate the security of the Dam system in the form of Common Criteria, develop safety goals to improve this safety, and focus on threats and risks to the dam SCADA system. Finally proposes an anomaly-based machine-learning framework for detecting malicious network attacks in the SCADA system of a dam. Three unsupervised classification algorithms are considered: hierarchical clustering, local outlier factor, and isolation forest. It is shown that the hierarchical clustering algorithm achieves the highest precision and F-score of the three algorithms. Overall, the results confirm the effectiveness of anomaly-based detection algorithms in enhancing the robustness of SCADA systems toward malicious attacks. At the same time, it complies with the security objectives of Common Criteria, achieving the safety and protection of the dam. © 2024 The Author(s). IET Networks published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： Internet of things

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A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images

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Neural Computing and Applications 2024年第21期36卷 12701-12723页

作者： El-Mawla, Nesma Abd Berbar, Mohamed A. El-Fishawy, Nawal A. El-Rashidy, Mohamed A. Communication and Electronics Engineering Department Nile Higher Institute for Engineering and Technology Mansoura Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Shebeen El-Kom Egypt Department of Computer Arab East Colleges Riyadh Saudi Arabia

Clinical decision support systems (CDSSs) can effectively detect illnesses such as breast cancer (BC) using a variety of medical imaging techniques. BC is a key factor contributing to the rise in the death rate among women worldwide. Early detection will lessen its impact, which may motivate patients to have quick surgical therapy. computer-aided diagnosis (CAD) systems are designed to provide radiologists recommendations to assist them in diagnosing BC. However, it is still restricted and limited, the interpretability cost, time consumption, and complexity of architecture are not considered. These limitations limit their use in healthcare devices. Therefore, we thought of presenting a revolutionary deep learning (DL) architecture based on recurrent and convolutional neural networks called Bi-xBcNet-96. In order to decrease carbon emissions while developing the DL model for medical image analysis and meet the objectives of sustainable artificial intelligence, this study seeks to attain high accuracy at the lowest computing cost. It takes into consideration the various characteristics of the pathological variation of BC disease in mammography images to obtain high detection accuracy. It consists of six stages: identifying the region of interest, detecting spatial features, discovering the effective features of the BC pathological types that have infected nearby cells in a concentrated area, identifying the relationships between distantly infected cells in some BC pathological types, weighing the extracted features, and classifying the mammography image. According to experimental findings, Bi-xBcNet-96 beat other comparable works on the benchmark datasets, attaining a classification accuracy of 98.88% in DDSM dataset, 100% in INbreast dataset with 5.08% and 0.3% improvements over the state-of-the-art methods, respectively. Furthermore, a 95.79% reduction in computing complexity was achieved. © The Author(s) 2024.

关键词： computer aided diagnosis

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Food ingredient recognition model via image and textual feature extraction and hybrid classification strategy

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International Journal of Reasoning-based Intelligent Systems 2024年第1期16卷 74-90页

作者： Madival, Sharanabasappa A. Jawaligi, Shivkumar S. Department of Computer Science and Engineering Faculty of Engineering and Technology Sharnbasva University India Department of Electronic and Communication Engineering Faculty of Engineering and Technology Sharnbasva University Karanataka Kalburgi India

This research work focuses on food recognition, especially, the identification of the ingredients from food images. Here, the developed model includes two stages namely: 1) feature extraction;2) classification. Initially, the image features and textual features will be extracted, where image features like SIFT and improved CNN-based deep features, textural features are extracted. Then, the hybrid classifier is used for the identification of food ingredients that combines the models like neural network (NN) as well as long short-term memory (LSTM). In order to make the accurate results, the weights of NN and LSTM are fine-tuned via the Chebyshev map evaluated teamwork optimisation (CME-TWO) algorithm. At the final stage, the primacy of the offered scheme is proven concerning varied metrics. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Feature extraction

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An amalgamation of integrated features with DeepSpeech2 architecture and improved spell corrector for improving Gujarati language ASR system

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International Journal of Speech Technology 2024年第1期27卷 87-99页

作者： Dua, Mohit Bhagat, Bhavesh Dua, Shelza Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics Communication and Engineering Punjab Engineering College Chandigarh India

Automatic Speech Recognition systems that convert language into written text have greatly transformed human–machine interaction. Although these systems have achieved results, in languages building accurate and reliable ASR models for low resource languages like Gujarati comes with significant challenges. Gujarati lacks data and linguistic resources, making developing high-performance ASR systems quite difficult. In this paper, we propose an approach to enhance the effectiveness of a Gujarati ASR model despite resources. We achieve this by incorporating integrated features such as Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC) utilizing the DeepSpeech2 architecture and implementing an improved spell correction technique based on the Bidirectional Encoder Representations from Transformers (BERT) algorithm. Our approach has demonstrated superiority over previous state-of-the-art methodologies through testing and evaluation. The experimental results demonstrate that our proposed method consistently reduces the Word Error Rate (WER) by 10–12 percentage points compared to the existing work, surpassing the most significant improvement of 5.87%. Our findings demonstrate the viability of developing accurate and dependable ASR systems for languages with limited resources, such as Gujarati. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Character recognition

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A novel Cosine-Cosine chaotic map-based video encryption scheme

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Journal of engineering and Applied Science 2024年第1期71卷 36页

作者： Kumari, Sweta Dua, Mohit Dua, Shelza Dhingra, Deepti Department of Computer Engineering National Institute of Technology Kurukshetra India Department of Electronics and Communication Engineering National Institute of Technology Kurukshetra India

The surge in online activities has led to the increasing popularity of sharing video data across diverse applications, including online education tutorials, social networking, video calling, and OTT platforms. Encryption prevents unauthorized access to the transmitted data over unreliable channels. The well-known features of chaos theory such as random behaviour, unpredictability, and initial parameters dependency facilitate its use in cryptography. Many security issues are faced by chaos-based cryptosystems because of their less complexity. Hence, a new Cosine-Cosine chaotic map characterized by intricate chaotic behaviour is designed in the current study. Additionally, we formulate an original video encryption scheme employing this Cosine-Cosine chaotic map. The encryption process involves five steps, beginning with the segmentation of the original video into frames based on its frame rate. In the second phase, a 384 bits pseudorandom key is generated that is further divided into three subkeys of 128 bits each. The novel Cosine-Cosine chaotic map-based sequence is generated. In the fourth step, red, green, and blue components are encrypted using the pseudorandom key and the chaotic sequence. In the last step, we combine encrypted frames to get cipher video. The security analysis validates that the proposed encryption protects against eavesdropping. © The Author(s) 2024.

关键词： Cryptography

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