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2024 International Conference on computer and Applications, ICCA 2024

作者： Alotaibi, Jawza Al Ja'am, Jihad M. Hoda, Mohammed Oryx Universal College in Partnership with Liverpool John Moores University Dept. of Computing and Data Science Doha Qatar Kuwait College of Science and Technology Dept. of computer Science and Engineering Doha Kuwait

ISBN: (纸本)9798350367560

This research study investigates human responses to malware attacks, focusing on attacker motivations, individual reactions, and key factors influencing user vulnerability and resilience. Using a structured survey questionnaire, the study gathered data from 104 participants with diverse demographics across various occupations. The data was analyzed using SPSS statistical software and Python-based machine learning algorithms. The survey included demographic information and responses to cybersecurity-related questions about human behavior and experience. Machine learning techniques identified ten critical factors as significant predictors of vulnerability to malware attacks. The prediction model indicated that 55 out of 104 participants were vulnerable, with higher vulnerability observed among female participants and those in non-IT professions. This study contributes valuable insights into the human factors influencing cybersecurity resilience, providing a foundation for developing effective strategies to mitigate malware attack vulnerability. © 2024 IEEE.

关键词： Cyber attacks

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DTRadar: Accelerating Search Process of Decision Trees in Packet Classification 28

DTRadar: Accelerating Search Process of Decision Trees in Pa...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Yu, Jiashuo Huang, Long Zhu, Longlong Zhang, Dong Wu, Chunming College of Computer and Data Science Fuzhou University Fuzhou China Zhicheng College Fuzhou University Fuzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350300482

Packet classification is an essential part of computer networks. Existing algorithms propose a partition process to address the memory explosion problem of the decision tree algorithm caused by the huge number of rules with multiple fields. However, the search process requires traversing multiple trees generated by the partition, which reduces the search efficiency. The existing algorithms take simple approaches to optimize the search process, which is low efficiency or high hardware overhead. In this paper, we propose DTRadar, a framework for expediting the decision tree packet lookup process. Its key idea is building an abstract One-Big-Tree(OBT) for multiple decision trees by establishing the middle data structure. DTRadar considers each decision tree as a splittable tree and organizes these subtrees by intermediate data structures. Extensive experiments show that DTRadar benefits existing decision tree-based solutions in classification time by 61.60%, and the memory footprint only increased by 4.21% on average. © 2023 IEEE.

关键词： Decision trees

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P4CTM: Compressed Traffic Pattern Matching Based on Programmable data Plane 28

P4CTM: Compressed Traffic Pattern Matching Based on Programm...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Lin, Hang Lin, Weiwei Lin, Jing Zhu, Longlong Zhang, Dong Wu, Chunming College of Computer and Data Science Fuzhou University Fuzhou China Zhicheng College Fuzhou University Fuzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350300482

Pattern matching is an important technology applied to many security applications. Most network service providers choose to compress network traffic for better transmission, which brings the challenges of compressed traffic matching. However, existing works focus on improving the performance of uncompressed traffic matching or only realize the compressed traffic matching on end-host that can not keep pace with the dramatic increase in traffic. In this paper, we present P4CTM, a proof-of-concept method to conduct efficient compressed traffic matching on the programmable data plane. P4CTM uses the two-stage scan scheme to skip some bytes of compressed traffic, the 2-stride DFA combines with the compression algorithm to condense the state space, and the wildcard match to downsize the match action tables in the programmable data plane. The experiment indicates that P4CTM skips 83.10% bytes of compressed traffic, condenses the state space by order of magnitude, and reduces most of the table entries. © 2023 IEEE.

关键词： Pattern matching

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A Network Function Virtualization Resource Allocation Model Based on Heterogeneous Computing 28

A Network Function Virtualization Resource Allocation Model ...

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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Chen, Hanze Xiao, Feng Cheng, Lingfei Zhu, Longlong Zhang, Dong College of Computer and Data Science Fuzhou University Fuzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Zhicheng College Fuzhou University Fuzhou China

ISBN: (纸本)9798350300482

With the continuous increase in the speed and quantity of network traffic, higher performance requirements are put forward for the NFV system. Traditional virtualization technology is limited by slowing down of increase in CPU performance. There has some research on the use of hardware to accelerate network functions. However, existing method only consider use one hardware for acceleration, but hardware itself has limitation. It is difficult to match the network functions and hardware characteristics by considering the combination of each hardware and CPU discretely. In this paper, we propose a resource allocation model of network function virtualization(NFV) based on heterogeneous computing, which can maximize the resource utilization and obtain the global optimal solution. Our experiments prove that the genetic algorithm solution method we propose can take into account both the solution accuracy and the solution speed, and obtain an accurate Pareto curve under the multi-objectives optimization model. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Late Breaking Results: Mixed-Cell-Height Detailed Placement under Multi-Cell Spacing Constraints 24

Late Breaking Results: Mixed-Cell-Height Detailed Placement ...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Zhu, Benchao Zeng, Zheng Chen, Jianli School of Microelectronics Fudan University Shanghai China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (纸本)9798400706011

As technology scales down, multi-cell spacing constraints are imposed by modern circuit designs. Previous works compromise solution quality to address the problem by transforming it into two-cell spacing constraints. In this paper, we propose a detailed placement algorithm considering multi-cell spacing constraints. First, an SAT-based multi-cell spacing violation reduction method is presented to reduce the number of violations with minimum displacement. Then, a window-based violation elimination method is adopted to resolve all the remaining violations. Finally, we refine the placement result with ILP to reduce the cell displacement. Compared with the state-of-the-art work, experimental results show that our algorithm achieves a 19% improvement in displacement and a 39% reduction in runtime. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

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EfficientNetB1 Deep Learning Model for Microscopic Lung Cancer Lesion Detection and Classification Using Histopathological Images

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computers, Materials & Continua 2024年第10期81卷 809-825页

作者： Rabia Javed Tanzila Saba Tahani Jaser Alahmadi Sarah Al-Otaibi Bayan AlGhofaily Amjad Rehman Department of Computer Science Lahore College for Women UniversityLahore54000Pakistan Artificial Intelligence&Data Analytics Lab. CCIS Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh145111Saudi Arabia

Cancer poses a significant threat due to its aggressive nature,potential for widespread metastasis,and inherent heterogeneity,which often leads to resistance to *** cancer ranks among the most prevalent forms of cancer worldwide,affecting individuals of all *** and accurate lung cancer detection is critical for improving cancer patients’treatment outcomes and survival *** examinations for lung cancer detection,however,frequently fall short of detecting small polyps and *** address these limitations,computer-aided techniques for lung cancer detection prove to be invaluable resources for both healthcare practitioners and patients *** research implements an enhanced EfficientNetB1 deep learning model for accurate detection and classification using histopathological *** proposed technique accurately classifies the histopathological images into three distinct classes:(1)no cancer(benign),(2)adenocarcinomas,and(3)squamous cell *** evaluated the performance of the proposed technique using the histopathological(LC25000)lung *** preprocessing steps,such as image resizing and augmentation,are followed by loading a pretrained model and applying transfer *** dataset is then split into training and validation sets,with fine-tuning and retraining performed on the training *** model’s performance is evaluated on the validation dataset,and the results of lung cancer detection and classification into three classes are *** study’s findings show that an enhanced model achieves exceptional classification accuracy of 99.8%.

关键词： Colon cancer EfficientNetB1 histopathological image processing transfer learning health risks

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NOMA Attack Prediction in Large-Scale Networks using Non-Linear Chaotic Belief Mechanism 3

NOMA Attack Prediction in Large-Scale Networks using Non-Lin...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Sangeetha, K. Suguna, M. Jebakumar Immanuel, D. Suje, S.A. Department of Computer Science and Engineering SNS College of Technology Tamil Nadu Coimbatore India Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Tamil Nadu Coimbatore India Department of Artificial Intelligence and Data Science Nandha Engineering College Tamil Nadu Erode India

ISBN: (纸本)9798331527549

Non-Orthogonal Multiple Access (NOMA) systems are becoming relevant in the fast-expanding terrain of large-scale networks because of their efficiency in concurrently managing many users. This is true since NOMA systems let numerous users concurrently be managed. On the other hand, the intricacy of these networks leaves them vulnerable to a wide spectrum of attacks, including the more advanced and erratic NOMA attacks on the network. These strikes could produce major disturbances that would compromise the quality of service and cast questions regarding the general network security. It has been demonstrated that the effective projection of these hazards is limited by standard linear and probabilistic techniques. This is true as contemporary methods fail to adequately capture the basic non-linear dynamics of these large-scale networks. This article offers a novel method for NOMA attack prediction by means of a non-linear chaotic belief process. The results are shown here. To recreate the uncertainty and intricate interactions inside the network, the proposed method which is the logistic map which in turn generates the sequences for ensuring the accurate iterative updates which in turn provides better scalability and precision. This integrates belief networks with chaos theory. More exactly, we capture the random and nonlinear aspect of network dynamics by building belief values indicating the likelihood of an attack by use of a chaotic map. After that, the belief values proliferate across the network in search of defects and project the probability of NOMA attacks. Effectiveness of the proposed method is demonstrated by test results on a simulated large-scale network simulation. With a prediction accuracy of 92.7%, the chaotic belief mechanism obtained much above the average accuracy of 78.4% of traditional linear prediction systems. Moreover, the proposed approach lowered the false positive rate to 5.3%, substantially below the rate of 12.8% applied in the standard ap

关键词： Large scale systems

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Group Signature with Time-Bound Keys for Secure E-health Record Sharing

Group Signature with Time-Bound Keys for Secure E-health Rec...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lu, Junze Hu, Chunqiang Ruan, Conghao Cai, Bin Xiang, Tao Chongqing University School of Big Data & Software Engineering China Chongqing University College of Computer Science China

ISBN: (纸本)9798350386226

With the advent of various mobile IoT devices, a large amount of e-health record (EHR) data has been generated. This data has great potential to improve medical research. However, there are many challenges regarding the sharing of medical data. Firstly, users are more inclined to interact anonymously. Secondly, verifying the validity of certificates in the case of anonymous interactions is challenging. In addition, it is necessary to uncover the identities of the actual interacting parties in the event of malicious behavior. Therefore, we address the above challenges and propose group signatures with time constraints to support anonymous and traceable EHR data sharing. First, we propose a group signature scheme that supports traceability. Second, to address the issue of validating anonymous certificates, we propose group signatures with time constraints that enable dynamic updates to validity. Through this, we can dynamically revoke group members. Lastly, security proofs and efficiency analyses demonstrate that our scheme is both secure and efficient. © 2024 IEEE.

关键词： Electronic health record

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ECCA:An Efficient Channel-and-Coordinate Attention Module for Image Stitching

ECCA:An Efficient Channel-and-Coordinate Attention Module fo...

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2023 China Automation Congress, CAC 2023

作者： Chen, Weikai Yu, Yuanlong Huang, Zhiyong Wu, Zhixin College of Computer and Data Science Science/College of Software Fuzhou University Fuzhou China Southwest Automation Research Institute Sichuan China

ISBN: (纸本)9798350303759

We propose a simple and effective attention module, called Efficient Channel-and-Coordinate Attention (ECCA), which can be applied to CNN-based image stitching models. Traditional image stitching models usually use deep convolutional neural networks, but as the depth increases, the information capacity of the model also increases, leading to unstable region of interest and degraded feature quality. Moreover, for similar features in different positions of two images, the model may mistakenly regard them as the same feature, resulting in incorrect stitching results. To address these issues, we design a multi-head attention mechanism that combines channels and coordinate positions. The module can enable the network to focus on more critical information among the numerous input information and extract higher quality features. Specifically, the module consists of two attention heads: local channel attention head and coordinate attention head. The local channel attention head calculates the correlation between each channel and its adjacent k channels, and the coordinate attention head calculates the correlation between each channel and the global channel according to the feature position in the image. The two attention heads obtain attention weights respectively, and multiply them with the original features to obtain the final output features. Our ECCA module can effectively improve the performance of existing state-of-the-art deep image stitching models. On the WarpedCOCO dataset, we apply the ECCA module to VFISnet and LB-UDHN networks, respectively improving their PSNR values by 0.63 and 1.92, and SSIM values by 5.7% and 5.1%. Code is available at https://***/chenwinkk/ECCA-Network © 2023 IEEE.

关键词： Convolutional neural networks

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Machine Learning in Healthcare: Prognosis Heart Disease Prediction 4

Machine Learning in Healthcare: Prognosis Heart Disease Pred...

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4th International Conference on Advancement in Electronics and Communication Engineering, AECE 2024

作者： Ramnath, M. Revathi, B. Selva Birunda, S. Usharani, C. Ramana, R. Tyson Masillamani, J. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Thamirabharani Engineernig College Department of Computer Science and Engineering Tirunelveli India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364729

The heart, being a crucial organ, necessitates meticulous care. Accurate information is essential for identifying heart-related disorders. Precise patient data is vital for hospitals to effectively predict and treat cardiac issues. Utilizing advanced Machine Learning algorithms, such as Logistic Regression, Random Forest, decision trees, K-Nearest Neighbours, Support Vector Machine, Naive Bayes along with statistical models, makes the accurate prediction of heart diseases feasible and accuracy comparison has been made. Finally, a better model has been developed to predict heart disease predictions. Medical practitioners can efficiently detect and address cardiac problems by leveraging these sophisticated algorithms, thereby maximizing the benefits of current medical technology. The application of these algorithms has notably improved patient health and the overall quality of care. Furthermore, hospitals to allocate the resources more precisely perform the predictive analytics more efficiently and enhance patient outcomes. By adopting these technological advancements, medical professionals can stay ahead of potential heart-related issues and deliver better care to their patients. © 2024 IEEE.

关键词： Logistic regression

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