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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Shi, Chenglong Geng, Haibin Jiang, Wenfeng Li, Jinbao Wu, Chao Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

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computer Modeling in Engineering & sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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JTEA: Implementing Semi-Supervised Entity Alignment Using Joint Teaching Strategies

JTEA: Implementing Semi-Supervised Entity Alignment Using Jo...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Lu, Kai Zhao, Jing Ding, Lichao Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan China Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method. © 2024 IEEE.

关键词： Mapping

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Sensor-based identification to detect counterfeit smartphones using Blockchain

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 16521-16538页

作者： Almuhaideb, Abdullah M. Alsunaidi, Shikah J. Department of Networks and Communications College of Computer Science and Information Technology SAUDI ARAMCO Cybersecurity Chair Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia Department of Computer Science College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam31441 Saudi Arabia

The rapid growth of the technology sector and the increasing need to own smartphones contributed to the increase in the rate of smartphones theft, as well as the sale of counterfeit or substandard devices. The Internet provides many tools and tutorials that show the criminal how to manipulate the identity of a stolen device to prevent tracking or detection, as well as to help him/her unlock/unblock the device. Consequently, the likelihood of recovering or finding stolen smartphones is significantly reduced. There are many challenges during the development of a security solution to this problem, the most important of which is the limited smartphone resources. In this paper, we propose a new device identification approach considering the challenges of the smartphone identification process. It utilizes the sensor calibration-based identity as a device fingerprint and the Blockchain technology to manage the fingerprint. The comparisons with related solutions showed that our proposed method can balance between all solution requirements including compatibility with resource-constrained devices, fulfilling of the device identification, and the security requirements to detect counterfeit smartphones. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Smartphones

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Analyzing the Performance of LoRa Using SX1278 and Dragino Module 12

Analyzing the Performance of LoRa Using SX1278 and Dragino M...

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12th International Conference on Information and Communication Technology, ICoICT 2024

作者： Tin Toong, Tai Yogarayan, Sumendra Fatimah Abdul Razak, Siti Ee Mae, Ang Azman, Afizan Rama, Kavila Faculty of Information Science and Technology Melaka Malaysia School of Computer Science Faculty of Innovation and Technology Taylors University Selangor Malaysia AceTeam Networks Sdn Bhd Selangor Malaysia

ISBN: (纸本)9798350363432

Wireless communication systems play a pivotal role in various applications, driving the need for robust and efficient modules. This paper addresses the challenges, methodologies, and outcomes of a project focused on evaluating the signal range and stability of the SX1278 and Dragino LoRa Shield modules. The project encompassed material sourcing, module operation familiarization, testing location selection, and overcoming programming tool limitations. The key objectives include determining the maximum achievable distance and assessing signal stability of the modules. The analysis of collected data revealed that the Dragino module demonstrated superior performance over the SX1278 module. This study contributes valuable insights into the comparative performance of these LoRa modules, offering implications for their practical deployment in wireless communication systems. © 2024 IEEE.

关键词： Wireless networks

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Abnormal event detection in surveillance videos through LSTM auto-encoding and local minima assistance

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Discover Internet of Things 2025年第1期5卷 1-25页

作者： Sengonul, Erkan Samet, Refik Abu Al-Haija, Qasem Alqahtani, Ali Alsemmeari, Rayan A. Alghamdi, Bandar Alturki, Badraddin Alsulami, Abdulaziz A. Department of Computer Engineering Ankara University Ankara06100 Turkey Department of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology PO Box 3030 Irbid22110 Jordan Department of Networks & Communications Engineering College of Computer Science & Information Systems Najran University Najran61441 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

Abnormal event detection in video surveillance is critical for security, traffic management, and industrial monitoring applications. This paper introduces an innovative methodology for anomaly detection in video data, encompassing three primary stages: preprocessing, feature learning, and anomaly detection. We employ background subtraction and noise reduction during preprocessing to refine the data. The feature-learning stage involves training an LSTM autoencoder to capture the essential features of normal video sequences. For anomaly detection, we map video data to a lower-dimensional space (latent code) and compare it against the distribution of codes from normal sequences. We determine regularity scores and identify local minima points exceeding a specified threshold while scrutinizing shadows between adjacent maxima to confirm and pinpoint anomalies. When tested on the CUHK Avenue and UMN datasets, our methodology demonstrated performance with AUCs of 93.8% and 94.1%, respectively, outperforming several baseline models. Our results show the high precision of our method that can detect anomalies, highlighting its potential advantages that it can achieve for enhancing systems of surveillance. © The Author(s) 2025.

关键词： Noise abatement

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A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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CSLP: Collaborative Solution to Long-Tail Problem and Popularity Bias in Sequential Recommendation

CSLP: Collaborative Solution to Long-Tail Problem and Popula...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Huang, Yan Yang, Zhenyu Hu, Wenyue Xu, Baojie Zhang, Zhibo Jinan250353 China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250353 China

ISBN: (纸本)9781665410205

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP. © 2024 IEEE.

关键词： Inverse problems

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

Knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Accurate Phase Detection for ZigBee Using Artificial Neural Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

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