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An Improved Hybrid Deep Learning Approach for Security Requirements Classification

computers, Materials & Continua 2025年第3期82卷 4041-4067页

作者： Shoaib Hassan QianmuLi Muhammad Zubair Rakan AAlsowail Muhammad Umair School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Faculty of Information Technology and Computer Science University of Central PunjabLahore5400Pakistan Computer Skills Self-Development Skills DevelopmentDeanship of Common First YearKing Saud UniversityRiyadh11362Saudia Arabia

As the trend to use the latestmachine learning models to automate requirements engineering processes continues,security requirements classification is tuning into the most researched field in the software engineering *** literature studies have proposed numerousmodels for the classification of security ***,adopting those models is constrained due to the lack of essential datasets permitting the repetition and generalization of studies employing more advanced machine learning ***,most of the researchers focus only on the classification of requirements with security *** did not consider other nonfunctional requirements(NFR)directly or indirectly related to *** has been identified as a significant research gap in security requirements *** major objective of this study is to propose a security requirements classification model that categorizes security and other relevant security *** use PROMISE_exp and DOSSPRE,the two most commonly used datasets in the software engineering *** proposed methodology consists of two *** the first step,we analyze all the nonfunctional requirements and their relation with security *** found 10 NFRs that have a strong relationship with security *** the second step,we categorize those NFRs in the security requirements *** proposedmethodology is a hybridmodel based on the ConvolutionalNeural Network(CNN)and Extreme Gradient Boosting(XGBoost)***,we evaluate the model by updating the requirement type column with a binary classification column in the dataset to classify the requirements into security and non-security *** performance is evaluated using four metrics:recall,precision,accuracy,and F1 Score with 20 and 28 epochs number and batch size of 32 for PROMISE_exp and DOSSPRE datasets and achieved 87.3%and 85.3%accuracy,*** proposed study shows an enhancement in metrics

关键词： Requirements engineering security requirements deep learning CNN XGBoost classification

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Behavioral insights into compression: a study of Move-to-Front-or-Middle deterministic online algorithm through sequence classification and characterization

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International Journal of Information technology (Singapore) 2025年第1期17卷 189-203页

作者： Baisakh Mohapatra, Hitesh Guru, Abhishek Department of Computer Science and Engineering Sambalpur University Institute of Information Technology Sambalpur India Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Engineering KIIT Deemed to Be University Bhubanseswar India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India

Since the list update problem was applied to data compression as an effective encoding technique, numerous deterministic algorithms have been studied and analyzed. A powerful strategy, Move-to-Front (MTF), involves moving an accessed item to the front of the list immediately. Theoretical analysis has demonstrated MTF’s potential, showing it to be 2-competitive through competitive analysis. This success has inspired researchers to develop various intriguing MTF variants and assess their performance both theoretically and experimentally. One such variant, Move-to-Front-or-Middle (MFM), has been experimentally shown that MFM performed better than the classic MTF on Calgary and Cantebury Datasets. However, the experimental performance alone does not provide sufficient insight into the internal behavior of the algorithm or explain why its performance surpasses MTF on certain datasets. Therefore, in this paper, we conduct a detailed behavioral study of MFM through sequence classification and characterization to gain deeper understanding of the algorithm and aims to reconcile the disparity between theoretical and experimental outcomes. This paper also by classifying and characterizing MFM’s performance across various request sequences, considering both the presence and absence of locality of reference. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Competitive analysis Deterministic list update algorithm List update problem Sequence classification

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Introduction to the Special Issue on Deep Learning for Robust Human Body Language Understanding

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ACM Transactions on Multimedia Computing, Communications and Applications 2025年第4期21卷 1-7页

作者： Guo, Dan McDaniel, Troy Wang, Shuhui Wang, Meng School of Computer and Information Hefei University of Technology Hefei China Department of Computer Science and Engineering Arizona State University TempeAZ United States Chinese Academy of Sciences Beijing China

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A Survey of Distributed Algorithms for Aggregative Games

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IEEE/CAA Journal of Automatica Sinica 2025年第5期12卷 859-871页

作者： Li, Huaqing Li, Jun Ran, Liang Zheng, Lifeng Huang, Tingwen College of Electronic and Information Engineering Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518107 China

Game theory-based models and design tools have gained substantial prominence for controlling and optimizing behavior within distributed engineering systems due to the inherent distribution of decisions among individuals. In non-cooperative settings, aggregative games serve as a mathematical framework model for the interdependent optimal decision-making problem among a group of non-cooperative players. In such scenarios, each player's decision is influenced by an aggregation of all players' decisions. Nash equilibrium (NE) seeking in aggregative games has emerged as a vibrant topic driven by applications that harness the aggregation property. This paper presents a comprehensive overview of the current research on aggregative games with a focus on communication topology. A systematic classification is conducted on distributed algorithm research based on communication topologies such as undirected networks, directed networks, and time-varying networks. Furthermore, it sorts out the challenges and compares the algorithms' convergence performance. It also delves into real-world applications of distributed optimization techniques grounded in aggregative games. Finally, it proposes several challenges that can guide future research directions. © 2014 Chinese Association of Automation.

关键词： Nash equilibrium

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Pruning techniques for artificial intelligence networks: a deeper look at their engineering design and bias: the first review of its kind

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Multimedia Tools and Applications 2025年第11期84卷 9591-9665页

作者： Mohanty, Lopamudra Kumar, Ashish Mehta, Vivek Agarwal, Mohit Suri, Jasjit S. School of Computer Science Engineering and Technology Bennett University Gr. Noida India Department of Computer Science ABES Engineering College UP Ghaziabad India Stroke Diagnostic and Monitoring Division AtheroPoint™ RosevilleCA United States

Trained Artificial Intelligence (AI) models are challenging to install on edge devices as they are low in memory and computational power. Pruned AI (PAI) models are therefore needed with minimal degradation in performance. This is the first study of its kind where we have characterized three types of PAI paradigms, namely, weight pruning (WP), channel pruning (CP), and hybrid pruning (HP). Further, the study also analyses the risk of bias (RoB) in PAI models, and explainable PAI (XPAI) networks. PRISMA model was adapted for the selection of the best 145 studies. The review had two hypotheses: (i) PAI had bias and (ii) the order of performance for the three PAI is: HP > CP > WP in terms of performance. Thirty-two AI attributes using each of two experts were categorized into four clusters, namely, architecture, optimization, dataset design, and scientific validation. The RoB was analyzed in these PAI studies using AP(ai)Bias 2.0 (AtheroPoint™, CA, USA), which was based on a scoring-and-ranking strategy. This was then compared against the Butterfly model, composed of radial and regional biases, and analyzed using a Venn diagram in three independent bins based on low-moderate and moderate-high cut-offs. Recommendations were laid out for low-bias studies. PAI studies that lack adequate AI attributes were more inclined to the RoB. It has been observed that 19.64%, 21.42%, and 58.92% studies were in low-bias, moderate-bias, and high-bias bins, respectively. Pruning made inference faster and reduced sparsity. We have observed low participation of the PAI studies in the XPAI framework. The study presented the challenges and five-step recommendations for mitigating bias in PAI models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Risk assessment

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An Encoding-Based Privacy-Preserving Security Control Strategy Under Stealthy Attacks

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 208-217页

作者： Liu, Jian Wang, Shuailong Zha, Lijuan Liu, Jinliang Tian, Engang Nanjing University of Finance and Economics School of Computer and Artificial Intelligence Nanjing210023 China Nanjing Forestry University School of Science Nanjing210037 China Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China

This paper focuses on the privacy-preserving security control issue for cyber-physical systems (CPSs) via an encoding-decoding communication scheme (EDCS). To considerably contemplate the delayed data acquisition and analysis, the integral measurement model is established, which is governed by the function related to system states within a certain period. The EDCS designed has a certain resistant to the non-ideal communication network with limited bandwidth capacity and fading channels. Then the controller is constructed by utilizing the decoding data. Furthermore, in the controller-to-actuator channel, assuming the stealthy false data injection attacks occur randomly and the injected pseudo data is associated with transmission data that has been eavesdropped. By virtue of deriving sufficient conditions, the CPS with EDCS can achieve input-to-state stability as well as the corresponding controller and observer gain parameters are acquired. Eventually, the effectiveness of the designed EDCS and control algorithm is illustrated by a case study involving an autonomous underwater vehicle system. © 2023 IEEE.

关键词： Encoding (symbols)

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Sufficient learning: mining denser high-quality pixel-level labels for edge detection

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Neural Computing and Applications 2025年第14期37卷 8245-8260页

作者： Chen, Xiaodiao Yang, Wenya Wu, Wen Tao, Xiuting Mao, Xiaoyang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Media and Design Hangzhou Dianzi University Hangzhou310018 China Department of Computer Science and Engineering University of Yamanashi Kofu400-8511 Japan

The success of deep neural networks can largely be attributed to large-scale datasets with accurate annotations. In many practical applications, labels are annotated by multiple annotators, resulting in ambiguous labels. To mitigate the impact of such labels, existing deep edge detectors tend only to exploit sparsely reliable samples with high-confident labels by simple voting mechanism. However, lacking sufficient pixel-level learnable samples would severely degrade detection performance. To combat this issue, this work designs a sufficient learning framework for edge detection (SLED) to sufficiently explore reliable samples for model learning, which consists of three stages: sample selection, label refinement, and retraining. Firstly, we propose a sample selection scheme founded on history prediction, which aims to identify more reliable samples and locate unreliable regions containing potentially reliable samples. Secondly, we design an efficient label refinement strategy based on a graph convolutional network (GCN) to propagate reliable label information to unreliable nodes. This strategy can generate "new training targets" with denser, reliable labels for all images. Finally, we retrain the network with these new targets, obtaining a more robust model. Experimental results on three widely used benchmarks show that the proposed detector outperforms all compared SOTA methods. Our code will be released at https://***/wenya1994/SLED. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Pixels

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Secured IIoT against trust deficit - A flexi cryptic approach

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Multimedia Tools and Applications 2025年第9期84卷 5625-5652页

作者： Padmapriya, V.M. Thenmozhi, K. Hemalatha, M. Thanikaiselvan, V. Lakshmi, C. Chidambaram, Nithya Rengarajan, Amirtharajan Department of Computer Sciences Marquette University MilwaukeeWI53233 United States School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613401 India Department of Electrical and Computer Engineering Faculty of Engineering King Abdulaziz University Jeddah22254 Saudi Arabia School of Electronics Engineering Vellore Institute of Technology Vellore632014 India

This research allows the secure surveillance approach for the Internet of Things (IoT) methodology to be developed by integrating wireless signalling and image encryption strategy. Since the Cloud Service Telco (CST) is a semi-trusted body in cloud services, user data is encrypted before uploading to a cloud server for data protection from disclosure. The flexibility of encrypted data sharing is essential for cloud storage users. This study investigates the Discrete Wavelet Transform (DWT) technique with modified Huffman compression and Elliptic Curve Cryptography (ECC). It encrypts and decrypts the data and enhances industrial security surveillance in transmission. It uses the wireless network’s next generation (5G or 6G) as uplink Single Carrier Frequency Division Multiple Access (SC-FDMA) strategies via the IoT. This study presented a novel approach to proposing hardware architecture for a secure web camera integrated with the Atmel in the mega AVR family (ATMEGA) microcontroller, suitable for IoT applications. The experimental results confirm the proposed model’s efficacy compared with existing robustness and security analysis algorithms. These systems are also used by implementing industry-standard protocols using IoTs to monitor industrial applications. The proposed framework can also minimise bandwidth, transmission cost, storage space, tracking data, and decisions about abnormal events such as potential fraud and extinguisher detection in surveilling applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： 5G mobile communication systems

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Diffusion Models Meet Network Management: Improving Traffic Matrix Analysis with Diffusion-Based Approach

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IEEE Transactions on Network and Service Management 2025年第2期22卷 1259-1275页

作者： Yuan, Xinyu Qiao, Yan Wei, Zhenchun Zhang, Zeyu Li, Minyue Zhao, Pei Hu, Rongyao Li, Wenjing Hefei University of Technology School of Computer Science and Information Engineering China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Macau University of Science and Technology Faculty of Information Technology China

Due to network operation and maintenance relying heavily on network traffic monitoring, traffic matrix analysis has been one of the most crucial issues for network management related tasks. However, it is challenging to reliably obtain the precise measurement in computer networks because of the high measurement cost, and the unavoidable transmission loss. Although some methods proposed in recent years allowed estimating network traffic from partial flow-level or link-level measurements, they often perform poorly for traffic matrix estimation nowadays. Despite strong assumptions like low-rank structure and the prior distribution, existing techniques are usually task-specific and tend to be significantly worse as modern network communication is extremely complicated and dynamic. To address the dilemma, this paper proposed a diffusion-based traffic matrix analysis framework named Diffusion-TM, which leverages problem-agnostic diffusion to notably elevate the estimation performance in both traffic distribution and accuracy. The novel framework not only takes advantage of the powerful generative ability of diffusion models to produce realistic network traffic, but also leverages the denoising process to unbiasedly estimate all end-to-end traffic in a plug-and-play manner under theoretical guarantee. Moreover, taking into account that compiling an intact traffic dataset is usually infeasible, we also propose a two-stage training scheme to make our framework be insensitive to missing values in the dataset. With extensive experiments with real-world datasets, we illustrate the effectiveness of Diffusion-TM on several tasks. Moreover, the results also demonstrate that our method can obtain promising results even with 5% known values left in the datasets. © 2004-2012 IEEE.

关键词： Level measurement

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ResDecode: Accelerating Large Language Models Inference via Residual Decoding Heads

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Big Data Mining and Analytics 2025年第4期8卷 779-793页

作者： Zeng, Ziqian Yu, Jiahong Pang, Qianshi Wang, Zihao Zhuang, Huiping Yu, Fan Shao, Hongen Zou, Xiaofeng Shien Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou 511442 China School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China School of Engineering The Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong Huawei Technologies Co. Ltd. Hangzhou 310000 China School of Future Technology South China University of Technology Guangzhou 511442 China

Large language Models (LLMs) have immense potential to enhance the capabilities of Cyber-Physical-Social Intelligence (CPSI) systems, enabling them to better engage with complex cyber, physical, and social environments. However, the high inference latency of LLMs, which is inherited from the autoregressive decoding process, hinders their wide application in CPSI systems. To address this challenge, current approaches have incorporated speculative decoding to enable parallel prediction of multiple subsequent tokens, thereby achieving inference acceleration. Nevertheless, the accuracy of these decoding heads falls short of the autoregressive decoding approach. In light of these limitations, we propose ResDecode, a novel speculative decoding method characterized by its efficient and accurate decoding heads. Within the lightweight draft model, we propose a residual decoding head to compensate for the full context encoder's limited capability on long-range dependencies, thus improving accuracy. ResDecode demonstrates impressive results, achieving a maximum speedup ratio of 3.2× on the MT-bench compared to vanilla autoregressive decoding. © 2018 Tsinghua University Press.

关键词： efficient inference Large Language Models (LLMs) speculative decoding

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