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Hybrid of Distributed Cumulative Histograms and Classification Model for Attack Detection

computer systems Science & engineering 2023年第5期45卷 2235-2247页

作者： Mostafa Nassar Anas M.Ali Walid El-Shafai Adel Saleeb Fathi E.Abd El-Samie Naglaa F.Soliman Hussah Nasser AlEisa Hossam Eldin H.Ahmed Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Alexandria Higher Institute of Engineering&Technology(AIET) AlexandriaEgypt Department of Computer Science Security Engineering LaboratoryPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Traditional security systems are exposed to many various attacks,which represents a major challenge for the spread of the Internet in the *** techniques have been suggested for detecting attacks using machine learning and deep *** significant advantage of deep learning is that it is highly efficient,but it needs a large training time with a lot of ***,in this paper,we present a new feature reduction strategy based on Distributed Cumulative Histograms(DCH)to distinguish between dataset features to locate the most effective *** histograms assess the dataset instance patterns of the applied features to identify the most effective attributes that can significantly impact the classification *** different models for detecting attacks using Convolutional Neural Network(CNN)and Long Short-Term Memory Network(LSTM)are also *** accuracy test of attack detection using the hybrid model was 98.96%on the UNSW-NP15 *** proposed model is compared with wrapper-based and filter-based Feature Selection(FS)*** proposed model reduced classification time and increased detection accuracy.

关键词： Feature selection DCH LSTM CNN security systems

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An Immutable Framework for Smart Healthcare Using Blockchain Technology

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computer systems Science & engineering 2023年第7期46卷 165-179页

作者： Faneela Muazzam A.Khan Suliman A.Alsuhibany Walid El-Shafai Mujeeb Ur Rehman Jawad Ahmad Department of Computer Science Quaid-i-Azam UniversityIslamabad45320Pakistan Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Computer Science Security Engineering LaboratoryPrince Sultan UniversityRiyadh11586Saudi Arabia James Watt School of Engineering University of GlasgowG128QQUK School of Computing Edinburgh Napier UniversityEdinburghEH105DTUK

The advancements in sensing technologies,information processing,and communication schemes have revolutionized the healthcare *** Healthcare Records(EHR)facilitate the patients,doctors,hospitals,and other stakeholders to maintain valuable data and medical *** traditional EHRs are based on cloud-based architectures and are susceptible to multiple cyberattacks.A single attempt of a successful Denial of Service(DoS)attack can compromise the complete healthcare *** article introduces a secure and immutable blockchain-based framework for the Internet of Medical Things(IoMT)to address the stated *** proposed architecture is on the idea of a lightweight private blockchain-based network that facilitates the users and hospitals to perform multiple healthcare-related operations in a secure and trustworthy *** efficacy of the proposed framework is evaluated in the context of service execution time and *** experimental outcomes indicate that the proposed design attained lower service execution time and higher throughput under different control parameters.

关键词： Blockchain technology healthcare applications cybersecurity services IoMT DoS EHR

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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

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engineering 2024年第2期33卷 178-189页

作者： Xiaoming Yuan Jiahui Chen Ning Zhang Qiang(John)Ye Changle Li Chunsheng Zhu Xuemin Sherman Shen Qinhuangdao Branch Campus Northeastern UniversityQinhuangdao 066004China State Key Laboratory of Integrated Services Networks&the Research Institute of Smart Transportation Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada SUSTech Institute of Future Networks Southern University of Science and TechnologyShenzhen 518055China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility *** order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client *** enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for *** problem is decoupled into two convex *** to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration *** on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their *** simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

关键词： Knowledge sharing Internet of Vehicles Federated learning Broad learning Reconfigurable intelligent surfaces Resource allocation

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Privacy-Preservation for Distributed Quantized k-means Clustering 59

Privacy-Preservation for Distributed Quantized k-means Clust...

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59th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2023

作者： Rikos, Apostolos I. Manitara, Nicolas E. Oliva, Gabriele Hadjicostis, Christoforos N. Boston University Division of Systems Engineering Department of Electrical and Computer Engineering BostonMA02215 United States University of Cyprus Department of Electrical and Computer Engineering Nicosia1678 Cyprus Università Campus Bio-Medico di Roma Unit of Automatic Control Department of Engineering Rome00128 Italy

ISBN: (纸本)9798350328141

In this paper, we enhance a distributed version of the well known k-means algorithm with privacy-preservation features. While ensuring that sensitive or confidential information remains undisclosed to unauthorized entities (in our case these entities are curious nodes within the network), we maintain the desirable features of the distributed algorithm: the transmitted values are quantized, which optimizes bandwidth utilization and alleviates communication bottlenecks, and nodes possess the ability to collectively determine when to terminate the algorithm, which enables the conservation of valuable resources. We introduce a novel privacy-preserving protocol that not only preserves the privacy of a node's state, but it also ensures confidentiality about its cluster affiliation (i.e., it does not reveal to curious nodes whether a node participates in the update calculation for a specific centroid value). Moreover, we precisely characterize topological conditions that guarantee privacy preservation for individual nodes. Our distributed algorithm allows the formation of exclusive clusters within a finite time frame on any static and strongly connected directed graph. © 2023 IEEE.

关键词： Directed graphs

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Differentially Private Online Federated Learning with Correlated Noise 63

Differentially Private Online Federated Learning with Correl...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Jiaojiao Zhu, Linglingzhi Johansson, Mikael Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science StockholmSE-100 44 Sweden Chinese University of Hong Kong Department of Systems Engineering and Engineering Management Hong Kong

ISBN: (纸本)9798350316339

We introduce a novel differentially private algorithm for online federated learning that employs temporally correlated noise to enhance utility while ensuring privacy of continuously released models. To address challenges posed by DP noise and local updates with streaming non-iid data, we develop a perturbed iterate analysis to control the impact of the DP noise on the utility. Moreover, we demonstrate how the drift errors from local updates can be effectively managed under a quasi-strong convexity condition. Subject to an (, δ) DP budget, we establish a dynamic regret bound over the entire time horizon, quantifying the impact of key parameters and the intensity of changes in dynamic environments. Numerical experiments confirm the efficacy of the proposed algorithm. © 2024 IEEE.

关键词： Federated learning

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Stationary, continuous, and discrete wavelet-based approach for secure medical image transmission

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Research on Biomedical engineering 2023年第1期39卷 167-178页

作者： Sayah, Moad Med Redouane, Kafi Med Amine, Khaldi Electrical Engineering Laboratory Department of Electronics Faculty of Sciences and Technology University of Kasdi Merbah Ouargla30000 Algeria Computer Science Department Faculty of Sciences and Technology University of Kasdi Merbah Ouargla30000 Algeria

Purpose: Watermarking is one of the techniques used to protect multimedia data, and images in particular, from malicious attacks by inserting a signature into these images. However, traditional watermarking schemes encounter limitations for sensitive images such as medical images. Methods: In this work, a robust and blind watermarking approach is proposed to secure medical images exchanged in telemedicine. Marking medical images allows precise authentication of the patient and avoids confusing scans and thus avoiding diagnostic errors that can have serious consequences. In this approach, the frequency content of the image is acquired using three different transforms. Schur decomposition is then applied to the low-frequency sub-bands. Finally, the obtained upper triangular matrix values are modifier to integrate the watermark bits. Result and conclusion: Imperceptibility and robustness experimental results show that the proposed methods adequately maintain a significant quality of watermarked images and are remarkably robust against several conventional attacks. Indeed, the average peak signal-to-noise ratio (PSNR) obtained is 44.90 dB which proves that the integration process generates few distortions to the original image. For the robustness, the obtained results show that the watermark is resistant to the most common attacks used in watermarking with a normalized cross correlation rate higher than 0.9 for the majority of the attacks. © 2023, The Author(s), under exclusive licence to The Brazilian Society of Biomedical engineering.

关键词： Discrete wavelet transforms

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DESIGN, MODELING AND SIMULATION OF CYBERCRIMINAL PERSONALITY-BASED CYBERATTACK CAMPAIGNS

DESIGN, MODELING AND SIMULATION OF CYBERCRIMINAL PERSONALITY...

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2024 Winter Simulation Conference, WSC 2024

作者： Shin, Jeongkeun Dobson, Geoffrey B. Carley, L. Richard Carley, Kathleen M. Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA United States Software and Societal Systems Department Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798331534202

Cybersecurity challenges are inherently complex, characterized by both advanced technical elements and complex aspects of human cognition. Although extensive research has explored how victims’ human factors affect their susceptibility to cyberattacks, the influence of cybercriminals’ personalities on the pattern of cyberattack campaigns and the resulting damage to organizations has not received equivalent attention. To bridge this research gap through computer simulation, we introduce a cyberattack campaign designer that enables modelers to construct cyberattack campaigns. This tool allows modelers to define how the process or pattern of the cyberattack campaign can change based on the cybercriminal’s personality and simulates how these personality differences influence the magnitude of cyberattack damage on the target organization. In this paper, through computer simulation, we demonstrate how cautious and reckless personalities result in variations in the cyberattack pattern and, consequently, affect the magnitude of cyberattack damage, despite identical cyberattack objectives and techniques used at each step. © 2024 IEEE.

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Spatio-temporal Attention Graph Convolutions for Skeleton-based Action Recognition 23rd

Spatio-temporal Attention Graph Convolutions for Skeleton-b...

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22nd Scandinavian Conference on Image Analysis, SCIA 2023

作者： Le, Cuong Liu, Xin Computer Vision and Pattern Recognition Laboratory School of Engineering Science Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Computer Vision Laboratory Department of Electrical Engineering Linköping University Linköping Sweden

ISBN: (纸本)9783031314346

In skeleton-based action recognition, graph convolutional networks (GCN) have been applied to extract features based on the dynamic of the human body and the method has achieved excellent results recently. However, GCN-based techniques only focus on the spatial correlations between human joints and often overlook the temporal relationships. In an action sequence, the consecutive frames in a neighborhood contain similar poses and using only temporal convolutions for extracting local features limits the flow of useful information into the calculations. In many cases, the discriminative features can present in long-range time steps and it is important to also consider them in the calculations to create stronger representations. We propose an attentional graph convolutional network, which adapts self-attention mechanisms to respectively model the correlations between human joints and between every time steps for skeleton-based action recognition. On two common datasets, the NTU-RGB+D60 and the NTU-RGB+D120, the proposed method achieved competitive classification results compared to state-of-the-art methods. The project’s GitHub page: STA-GCN. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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Towards Simpler Modelling Expressions for the Mechanical Characterization of Soft Materials

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Micro and Nanosystems 2024年第3期16卷 172-182页

作者： Kontomaris, Stylianos-Vasileios Malamou, Anna Stylianou, Andreas Chliveros, Georgios Faculty of Engineering and Architecture Metropolitan College Athens Greece BioNanoTec LTD Nicosia Cyprus Radar Systems and Remote Sensing Lab School of Electrical & Computer Engineering National Technical University of Athens Athens Greece Department of Mechanical Engineering Faculty of Engineering School of Sciences European University Cyprus Nicosia2404 Cyprus

Aims: The aim of this paper is to develop a new, simple equation for deep spherical indentations. Background: The Hertzian theory is the most widely applied mathematical tool when testing soft materials because it provides an elementary equation that can be used to fit force-indentation data and determine the mechanical properties of the sample (i.e., its Young’s modulus). However, the Hertz equation is only valid for parabolic or spherical indenters at low indentation depths. For large indentation depths, Sneddon’s extension of the Hertzian theory offers accurate force-indentation equations, while alternative approaches have also been developed. Despite ongoing mathematical efforts to derive new accurate equations for deep spherical indentations, the Hertz equation is still commonly used in most cases due to its simplicity in data processing. Objective: The main objective of this paper is to simplify the data processing for deep spherical indentations, primarily by providing an accurate equation that can be easily fitted to force-indentation data, similar to the Hertzian equation. Methods: A simple power-law equation is derived by considering the equal work done by the indenter using the actual equation. Results: The mentioned power-law equation was tested on simulated force-indentation data created using both spherical and sphero-conical indenters. Furthermore, it was applied to experimental force-indentation data obtained from agarose gels, demonstrating remarkable accuracy. Conclusion: A new elementary power-law equation for accurately determining Young’s modulus in deep spherical indentation has been derived. © 2024 Bentham Science Publishers.

关键词： Spheres

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On the Design of Novel Attention Mechanism for Enhanced Efficiency of Transformers 24

On the Design of Novel Attention Mechanism for Enhanced Effi...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Jha, Sumit Kumar Jha, Susmit Ewetz, Rickard Velasquez, Alvaro Computer Science Department Florida International University MiamiFL United States Computer Science Laboratory Sri International Menlo ParkCA United States Electrical and Computer Engineering University of Central Florida OrlandoFL United States Department of Computer Science University of Colorado Boulder BoulderCO United States

ISBN: (纸本)9798400706011

We present a new xor-based attention function for efficient hardware implementation of transformers. While the standard attention mechanism relies on matrix multiplication between the key and the transpose of the query, we propose replacing the computation of this attention function with bitwise xor operations. We mathematically analyze the information-theoretic properties of the standard multiplication-based attention, demonstrating that it preserves input entropy, and then computationally show that the xor-based attention approximately preserves the entropy of its input despite small variations in correlations between the inputs. Across various admittedly simple tasks, including arithmetic, sorting, and text generation, we show comparable performance to baseline methods using scaled GPT models. The xor-based computation of the attention function shows substantial improvement in power consumption, latency, and circuit area compared to the corresponding multiplication-based attention function. This hardware efficiency makes xor-based attention more compelling for the deployment of transformers under tight resource constraints, opening new application domains in sustainable energy-efficient computing. Additional optimizations to the xor-based attention function can further improve efficiency of transformers. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Distribution transformers

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