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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Song, Jiaxin Ju, Ying Liu, Lei Pei, Qingqi Wu, Celimuge Jan, Mian Ahmad Mumtaz, Shahid School of Telecommunications Engineering Xidian University Xi'an710071 China Graduate School of Informatics and Engineering The University of Electro-Communications Chofu182-8585 Japan Abdul Wali Khan University Mardan Department of Computer Science Mardan23200 Pakistan Silesian University of Technology Department of Applied Informatics Akademicka16 44-100 Poland Nottingham Trent University Engineering Department Nottingham999020 United Kingdom

ISBN: (纸本)9781665494274

Massive computing tasks of various applications have been generated in 6G space-air-ground integrated networks, and need to be transmitted securely and reliably. Nevertheless, the mobility of satellites and the untrusted nodes bring new challenges to the routing scheme design in low earth orbit (LEO) satellite networks. To improve the system trust and elevate the service quality, this paper proposes a fully distributed trustworthy load-balancing routing scheme for satellite services with a multi-agent dueling double deep Q network (D3QN)-based learning algorithm. Our scheme organizes multiple agents to generate hop-by-hop routes and makes decisions based on the trust value of the nodes, which has good scalability to deploy on various satellite constellations and can meet the trust requirements of the services. Besides, we add a variable delay constraint into the load minimization objective to meet various delay-sensitive satellite quality of service (QoS) requirements. We demonstrate that the proposed scheme dramatically reduces the link queue utilization rate and enhances the system capability of handling delay-sensitive services. The packet loss rate of our scheme is 24% lower than that of the benchmark scheme when the system has 30% malicious nodes. © 2023 IEEE.

关键词： Satellites

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Secure Energy Aware Power Control in Consumer Internet of Things With Semi Grant Free NOMA

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Abbas, Sohail Fayaz, Muhammad Ghandoura, Abdulrahman Khan, Muhammad Zahid Rehman, Ateeq Ur Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah UAE Department of Computer Science and IT University of Malakand Pakistan Applied College Department of Engineering and Applied Sciences Umm Al-Qura University Makkah Saudi Arabia Department of computing Staffordshire University UK

The Consumer Internet of Things (CIoT), a key aspect of the IoT, aims to integrate smart technologies into everyday life. In order to improve the spectral efficiency and provide massive connectivity to IoT networks, non-orthogonal multiple access (NOMA) variants like semi-grant-free (SGF) NOMA are employed. This paper aims to maximize secrecy energy efficiency (EE) for SGF-NOMA enabled CIoT in the presence of untrusted users (eavesdroppers) by utilizing a single-agent multi-agent deep reinforcement learning (SAMA-DRL) algorithm to overcome scalability and expensive learning issues. Given the limited long-distance transmission capabilities of CIoT devices, which typically have low transmit power, relay nodes are used to decode and forward data from grant-free (GF) users to the base station. Moreover, to enhance the coverage for GF users, the K-nearest neighbors (KNN) algorithm is utilized to place the relay nodes at an optimal positions. Furthermore, we design a collaborative contribution reward system to discourage user (agent) laziness. Simulation results show that the proposed SAMA-DRL-based SGF-NOMA algorithm for CIoT networks is more effective than baseline algorithms, achieving a 20% increase in secrecy EE compared to DRL-based SGF-NOMA without KNN. Moreover, the proposed scheme outperforms benchmark schemes in terms of EE across different radii. Additionally, we show that the proposed algorithm with quality of service based successive interference cancellation (SIC) is more power efficient as compared to conventional SIC decoding order. IEEE

关键词： Quality of service

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Comparative Analysis of Memory-Based Collaborative Filtering and Deep Learning Models for Resolving Cold Start and Data Sparsity Issues in E-commerce Recommender Systems

Comparative Analysis of Memory-Based Collaborative Filtering...

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applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Amrithkala M Shetty Manjaiah D H Mohammed Fadhel Aljunid Yogesh K M Department of Computer Science Mangalore University Karnataka India Computer and Informatics Center Thamar University Thamar Yemen Department of Computer Science and Engineering M S Ramaiah University of Applied Sciences

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

In today’s digital landscape, where online activities and e-commerce play pivotal roles, Recommender Systems (RS) have become indispensable tools for enhancing user experiences by providing personalized recommendations. However, collaborative filtering (CF) algorithms often face challenges such as the cold start problem and data sparsity. This work addresses these challenges using memory-based CF approaches such as Singular Value Decomposition (SVD), SVD++, K-Nearest Neighbors (KNN) Baseline, and K-Nearest Neighbors (KNN) Basic. Hyperparameters for these algorithms are optimized using GridSearchCV with cross-validation values set to 5, 10, and 15. Additionally, Deep Learning (DL) models including LSTM, BiLSTM, Att+BiLSTM, User-Product Fusion (UPF), and Deep Collaborative Filtering (DCF) are explored. The BiLSTM model achieves a lower Root Mean Squared Error (RMSE) of 1.1224, while the DCF model achieves a Mean Absolute Error (MAE) of 0.6897. Among the evaluated models, SVD++ achieves an RMSE of 0.9881 and an MAE of 0.7003 with cross-validation set to 15, indicating superior predictive accuracy. The best performance is observed with cross-validation set to 15. These results highlight the effectiveness of optimized memory-based CF algorithms in overcoming the cold start and data sparsity issues, leading to more accurate and reliable recommendations. The findings suggest that memory-based CF algorithms, specifically SVD++, can significantly enhance the development of robust recommender systems, ultimately improving user experience in e-commerce and other online platforms.

关键词： Deep learning Accuracy Collaborative filtering Computational modeling Nearest neighbor methods Prediction algorithms User experience Electronic commerce Recommender systems Long short term memory

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Analysis of the effectiveness of the optimization processes of trans active power system management with distributed multi-purpose energy resources

Analysis of the effectiveness of the optimization processes ...

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2024 International Conference on Artificial Intelligence, computer, Data Sciences, and Applications, ACDSA 2024

作者： Khilenko, Volodymyr Bielokha, Halyna Denysiuk, Serhii Stepanov, Oleksii Kotuliak, Ivan Slovak University of Technology in Bratislava Institute of Computer Engineering and Applied Informatics Faculty of Informatics and Information Technologies Bratislava Slovakia National Technical University of Ukraine "igor Sikorsky Kyiv Polytechnic Institute" Power Supply Department Kyiv Ukraine National University of Life and Environmental Sciences Ukr Article Acdsa Department of Information Technologies Khilenko ID 414 Kyiv09-02-2024 Ukraine

ISBN: (纸本)9798350394528

The article the efficiency of the Microgrid network when transitioning to a transactive power system that uses control algorithms called to optimize the distribution of power between sources of distributed generation and to minimize the time of decision-making about mode switching and at the corresponding change of the power system architecture. Tables and graphs of the conducted modeling are presented and the efficiency of this control-optimized system is calculated in comparison with the power system where uniform distribution of load power is used. © 2024 IEEE.

关键词： Distributed power generation

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Employing Deep Neural Networks for Real-Time Anomaly Detection and Mitigation in IoT-Based Smart Grid Cybersecurity Systems 3

Employing Deep Neural Networks for Real-Time Anomaly Detecti...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Quraishi, Aadam Rusho, Maher Ali Prasad, Anurag Keshta, Ismail Rivera, Richard Bhatt, Mohammed Wasim Intervention Treatment Institute Research HoustonTX United States University Of Colorado Boulder Department Of Lockheed Martin Engineering Management BoulderCO United States Gurugram India College Of Applied Sciences AlMaarefa University Computer Science And Information Systems Department Riyadh Saudi Arabia Escuela Politécnica Nacional Department Of Informatics And Computer Science Quito Ecuador Model Institute Of Engineering And Technology J&K Jammu India

ISBN: (纸本)9798350318609

This research introduces a novel anomaly detection framework for IoT -based Smart Grid Cybersecurity Systems. Leveraging autoencoders, LSTM networks, GANs, SOMs, and transfer learning, our approach achieves superior precision, recall, and execution time compared to existing methods. Visualizations and an ablation study further validate the method's efficiency, emphasizing the critical roles of attention mechanisms and transfer learning. This comprehensive solution addresses the dynamic challenges of smart grid cybersecurity, offering a versatile and adaptive anomaly detection mechanism for real-world applications. This indicates the real-time efficacy of our anomaly detection method. Through our study of ablation and all aspects of computing, we discovered that attention processes and transfer learning facilitate faster problem solving in a dynamic smart grid. Our method is distinct and adaptable enough to address every problem arising from the discovery of anomalies in IoT-driven Smart Grid Cybersecurity Systems. © 2024 IEEE.

关键词： Anomaly detection

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A Fused Machine Learning Approach for Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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A Hybrid Deep Learning Approach for Android Malware Detection 6th

A Hybrid Deep Learning Approach for Android Malware Detectio...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Ravi, Renjith V. Goyal, S.B. Rajawat, Anand Singh Shaari, Rosnizah Bt Verma, Chaman Singh, Pradeep Kumar School of Engineering and Applied Sciences Rashtriya Raksha University Gujrat Gandhinagar382305 India City University Petaling Jaya Malaysia School of Computer Sciences and Engineering Sandip University Nashik Nashik India City University Petaling Jaya46100 Malaysia Department of Media and Educational Informatics Faculty of Informatics Eötvös Loránd University Budapest1053 Hungary Department of CSE Central University of Jammu J&K India

ISBN: (纸本)9789819734412

The task of detecting malware on Android devices has become increasingly critical in the light of the widespread use of mobile devices and the escalating menace posed by malicious software. In the present scholarly publication, we put forth an innovative methodology for identifying malicious software within Android applications. Our proposed technique involves the utilisation of a hybrid recurrent neural network-convolutional neural network (RNN-CNN) architecture. The hybrid model effectively utilises the inherent advantages of both recurrent neural networks (RNNs) and convolutional neural networks (CNNs) in order to enhance the precision and efficacy of malware detection. We present a comprehensive methodology, including the architecture of the hybrid model and the CNN and RNN component parameters. The experimental findings serve to substantiate the efficacy of the proposed methodology in accurately discerning instances of Android malware. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Android malware

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Identifying Cell Types in Single-Cell Multimodal Omics Data via Joint Embedding Learning 15

Identifying Cell Types in Single-Cell Multimodal Omics Data ...

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15th International Conference on Knowledge and Systems engineering, KSE 2023

作者： Do, Van Hoan Canzar, Stefan Le Quy Don Technical University Center for Applied Mathematics and Informatics Hanoi Viet Nam Huck Institutes of the Life Sciences The Pennsylvania State University University Department of Computer Science and Engineering ParkPA16802 United States

ISBN: (纸本)9798350329742

Emerging single-cell technologies profile different modalities of data in the same cell, providing opportunities to study cellular population and cell development at a res-olution that was previously inaccessible. The first and most fundamental step in analyzing single-cell multimodal data is the identification of the cell types in the data using clustering analysis and classification. However, combining different data modalities for the classification task in multimodal data remains a computational challenge. We propose an approach for identifying cell types in multimodal omics data via joint dimensionality reduction. We first introduce a general framework that extends loss based dimensionality reduction methods such as nonnegative matrix factorization and UMAP to multimodal omics data. Our approach can learn the relative contribution of each modality to a concise representation of cellular identity that enhances discriminative features and decreases the effect of noisy features. The precise representation of the multimodal data in a low dimensional space improves the predictivity of classification methods. In our experiments using both synthetic and real data, we show that our framework produces unified embeddings that agree with known cell types and allows the predictive algorithms to annotate the cell types more accurately than state-of-the-art classification methods. © 2023 IEEE.

关键词： Cells

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Security Standards and use of Cyberinfrastructure Systems in Clinical Research 23

Security Standards and use of Cyberinfrastructure Systems in...

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2023 Practice and Experience in Advanced Research Computing, PEARC 2023

作者： Shankar, Anurag Hancock, David Michael, Scott Huber, Laura Butler, Joseph Henschel, Robert Stewart, Craig Center for Applied Cybersecurity Research Indiana University BloomingtonIN United States Research Technologies Indiana University BloomingtonIN United States Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9781450399852

Many areas of biomedical research, including precision health research, inherently involve the use of IT systems properly secured to store and analyze protected health information (PHI). Managing cybersecurity policies in ways that allow such storage and analysis of PHI on computing systems is involved, but so doing can facilitate important research. In 2005, Indiana University established the ability to analyze PHI (HIPAA alignment) as the minimal security level for its High Performance Computing (HPC) systems and research storage systems. Starting in 2010, REDCap was also offered as a central service, providing capabilities for conducting surveys that collect PHI and storing those data in ways that align with HIPAA. Usage data shows that providing computational and data resources aligned with HIPAA has increased the use of advanced research cyberinfrastructure over time. We explain some of our tactics in providing secure environments for handling protected health information and offer suggestions that other cyberinfrastructure centers may find useful in setting strategies for serving medical researchers. © 2023 ACM.

关键词： Clinical research

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