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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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ConsRec: Learning Consensus Behind Interactions for Group Recommendation 23

ConsRec: Learning Consensus Behind Interactions for Group Re...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Wu, Xixi Xiong, Yun Zhang, Yao Jiao, Yizhu Zhang, Jiawei Zhu, Yangyong Yu, Philip S. Shanghai Key Laboratory of Data Science School of Computer Science Fudan University Shanghai China University of Illinois Urbana-ChampaignIL United States Ifm Lab Department of Computer Science University of California DavisCA United States

ISBN: (纸本)9781450394161

Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task. Existing group recommendation methods usually infer groups' preferences via aggregating diverse members' interests. Actually, groups' ultimate choice involves compromises between members, and finally, an agreement can be reached. However, existing individual information aggregation lacks a holistic group-level consideration, failing to capture the consensus information. Besides, their specific aggregation strategies either suffer from high computational costs or become too coarse-grained to make precise predictions. To solve the aforementioned limitations, in this paper, we focus on exploring consensus behind group behavior data. To comprehensively capture the group consensus, we innovatively design three distinct views which provide mutually complementary information to enable multi-view learning, including member-level aggregation, item-level tastes, and group-level inherent preferences. To integrate and balance the multi-view information, an adaptive fusion component is further proposed. As to member-level aggregation, different from existing linear or attentive strategies, we design a novel hypergraph neural network that allows for efficient hypergraph convolutional operations to generate expressive member-level aggregation. We evaluate our ConsRec on two real-world datasets and experimental results show that our model outperforms state-of-the-art methods. An extensive case study also verifies the effectiveness of consensus modeling. © 2023 ACM.

关键词： Data mining

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Image inpainting via Smooth Tucker decomposition and Low-rank Hankel constraint

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International Journal of computers and Applications 2023年第6期45卷 421-432页

作者： Cai, Jing Jiang, Jiawei Wang, Yibing Zheng, Jianwei Xu, Honghui Department of Forensic Science Zhejiang Police College Hangzhou China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Hangzhou China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Image inpainting, aiming at exactly recovering missing pixels from partially observed entries, is typically an ill-posed problem. As a powerful constraint, low-rank priors have been widely applied in image inpainting to transform such problems into well-posed ones. However, the low-rank assumption of original visual data is only in an approximate mode, which in turn results in suboptimal recovery of fine-grained details, particularly when the missing rate is extremely high. Moreover, a single prior cannot faithfully capture the complex texture structure of an image. In this paper, we propose a joint usage of Smooth Tucker decomposition and Low-rank Hankel constraint (STLH) for image inpainting, which enables simultaneous capturing of the global low-rankness and local piecewise smoothness. Specifically, based on the Hankelization operation, the original image is mapped to a high-order structure for capturing more spatial and spectral information. By employing Tucker decomposition for optimizing the Hankel tensor and simultaneously applying Discrete Total Variation (DTV) to the Tucker factors, sharper edges are generated and better isotropic properties are enhanced. Moreover, to approximate the essential rank of the Tucker decomposition and avoid facing the uncertainty problem of the upper-rank limit, a reverse strategy is adopted to approximate the true rank of the Tucker decomposition. Finally, the overall image inpainting model is optimized by the well-known alternate least squares (ALS) algorithm. Extensive experiments show that the proposed method achieves state-of-the-art performance both quantitatively and qualitatively. Particularly, in the extreme case with 99% pixels missed, the results from STLH are averagely ahead of others at least 0.9dB in terms of PSNR values. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Pixels

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Joint Power Allocation and Task Scheduling for Data Offloading in Non-Geostationary Orbit Satellite Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2882-2896页

作者： He, Lijun Jia, Ziye Wang, Juncheng Lansard, Erick Han, Zhu Yuen, Chau China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Southeast University National Mobile Communications Research Laboratory Nanjing211111 China Hong Kong Baptist University Department of Computer Science Kowloon Tsai Hong Kong Nanyang Technological University School of Electrical and Electronics Engineering Jurong West 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In Non-Geostationary Orbit Satellite Networks (NGOSNs) with a large number of battery-carrying satellites, proper power allocation and task scheduling are crucial to improving data offloading efficiency. In this work, we jointly optimize power allocation and task scheduling to achieve energy-efficient data offloading in NGOSNs. Our goal is to properly balance the minimization of the total energy consumption and the maximization of the sum weights of tasks. Due to the tight coupling between power allocation and task scheduling, we first derive the optimal power allocation solution to the joint optimization problem with any given task scheduling policy. We then leverage the conflict graph model to transform the joint optimization problem into an Integer Linear Programming (ILP) problem with any given power allocation strategy. We explore the unique structure of the ILP problem to derive an efficient semidefinite relaxation-based solution. Finally, we utilize the genetic framework to combine the above special solutions as a two-layer solution for the original joint optimization problem. Simulation results demonstrate that our proposed solution can properly balance the reduction of total energy consumption and the improvement of the sum weights of tasks, thus achieving superior system performance over the current literature. © 2004-2012 IEEE.

关键词： Geostationary satellites

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Research and Design of Machine Vision-based Workpiece Defect Sorting Robot

Research and Design of Machine Vision-based Workpiece Defect...

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2024 International Conference on Intelligent Computing and Robotics, ICICR 2024

作者： Fu, Qingyu Yan, Hao He, Zhe Li, Wenyao Zang, Wenjing Zhu, Cheng Department of Automation Tianjin University of Commerce Tianjin China Computer Science Department Tianjin University of Commerce Tianjin China Automation Laboratory Tianjin University of Commerce Tianjin China

ISBN: (纸本)9798350372274

With the continuous improvement of industrial automation level, machine vision technology has been widely applied in the field of workpiece defect detection and sorting. In response to this demand, this paper proposes and designs a machine vision-based workpiece defect sorting system. This system integrates advanced technologies such as camera calibration, image preprocessing, and deep learning, which can efficiently and accurately identify workpiece defects and achieve automatic sorting. To verify the performance of the system, an experimental platform close to actual industrial scenarios was built, and extensive tests were conducted on workpieces of different types and batches. The experimental results show that the system has good robustness and reliability in complex environments, with a defect detection rate of over 98% and a sorting efficiency more than three times that of manual methods. Compared with traditional manual inspection methods, the machine vision sorting system significantly improves production efficiency and product quality, providing a feasible technical solution for the intelligent development of industries such as discrete manufacturing, with broad application prospects. In the future, machine vision technology is expected to play an important role in more industrial scenarios, promoting intelligent and high-quality development in manufacturing. ©2024 IEEE.

关键词： Machine vision

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TIoT: A Trust by Design Architecture for Secure and Private Internet of Things (IoT) 21

TIoT: A Trust by Design Architecture for Secure and Private ...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Alsheakh, H. Mahdi, Tareq Nasser Kadhm, Mustafa S. Al Shammari, Abeer Abdulsahib, Fatimah I. Taher, Hazeem B Algriree, Waleed Alobaidi, Marwa Q. Mohammed, Hayder Dhafer College of Science Mustansiriyah University Department of Computer Science Baghdad Iraq College of Pharmacy Mustansiriyah University Clinical Laboratory Science Baghdad Iraq Ministry of Science and Technology Baghdad Iraq Szechenyi Istvan Egyetem Department of Computer Science Gyor9026 Hungary University of Thi-Qar Department of Computer Science Thi-Qar Iraq University Putra Malaysia Dep. of Wireless Communication and Network Engineering Selangor Malaysia National Engineering School of Sfax Sfax University Sfax Tunisia College of Pharmacy Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9798350374131

The Internet of Things (loT) facilitates the interconnection of a vast array of devices, services, and individuals for data exchange in multi-domain loT ecosystems, the social, physical, and cyber domains. That emerges various security, privacy, and trust issues, such as vulnerabilities, threats, and attacks. Addressing these challenges requires innovative solutions. New controls (solutions) are needed. Trust by design can be one of these controls, supporting security and privacy (among other system components). This paper proposes a Trusted loT (TloT), a trust-by-design model, and highlights the author's principal contributions. First, it advocates for implementing Tlo T through trust services. Second, it outlines an architecture for using trust by design in loT, including the design of its top-level structure and activities. Minor contributions include overviews of loT and trust, as well as looking at security and privacy in terms of security services and privacy services, respectively. © 2024 IEEE.

关键词： Internet of things

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Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

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8th International Conference on Data science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

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Learning-Based Problem Reduction for Large-Scale Uncapacitated Facility Location Problems 13

Learning-Based Problem Reduction for Large-Scale Uncapacitat...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhang, Shuaixiang Yang, Yixuan Tong, Hao Yao, Xin Lingnan University Department of Computing and Decision Sciences Hong Kong Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

The uncapacitated facility location problem (UFLP) is a well-known combinational optimization problem, attracting numerous heuristic and meta-heuristic methods. However, these effective algorithms still encounter challenges when solving large-scale UFLP instances. To deal with the thousands of decision variables, one possible approach is to predict and remove unimportant facilities and thus decreases the dimensionality of problem instances. This paper explores the problem reduction of large-scale instances of UFLP. We propose suitable features of facilities to construct machine learning models for UFLP by learning from small instances. Based on this model, we introduce a novel problem reduction strategy-based optimization framework, and apply it to large instances. Through comprehen-sive experiments, we show that the proposed problem reduction strategy can effectively transform the UFLP instances to smaller ones. The performance of existing solution methods can be significantly enhanced, especially on large-scale instances. Under the proposed framework, we also show the generalization abilities of our models, which can be improved further. © 2024 IEEE.

关键词： Adversarial machine learning

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Pre-processing of RDF data for METIS partitioning

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International Journal of Metadata, Semantics and Ontologies 2023年第2期16卷 152-171页

作者： Benhamed, Siham Nait-Bahloul, Safia LITIO Laboratory University Oran 1 Ahmed Ben Bella Oran Algeria Department of Computing and Mathematics University Abdelhamid Ibn Badis – Mostaganem Mostaganem Algeria LITIO Laboratory Department of Computer Science University Oran 1 Ahmed Ben Bella Oran Algeria

The partitioning of RDF data on a large scale allows generating a set of RDF data subgraphs. METIS is a graph partitioning technique that minimises the cost of partitioning. METIS applies, among other things, to RDF graphs. However, the semantics introduced in the description of RDF data is not taken into account in the partitioning process in METIS. For this, we propose in this paper a step of pre-processing RDF data before partitioning these data. The objective of this step is to improve the quality of semantic partitioning of RDF graphs. The evaluation of the RDF pre-processing step for METIS was performed on real and synthetic data. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Semantics

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PNESR-DDI: An Effective Drug-Drug Interaction Prediction Model Based on Pretraining Method and Enhanced Subgraph Reconstruction

PNESR-DDI: An Effective Drug-Drug Interaction Prediction Mod...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Chen, Ke Han, Xiaosong Li, Xiaoran Liang, Yanchun Xu, Dong Guan, Renchu Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Software Changchun China Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun China Zhuhai College of Science and Technology Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Science Zhuhai China University of Missouri Christopher S. Bond Life Sciences Center Department of Electrical Engineering and Computer Science Columbia United States

ISBN: (纸本)9798350386226

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy. © 2024 IEEE.

关键词： Knowledge graph

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