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Generative AI based Secure Wireless Sensing for ISAC Networks

IEEE Transactions on Information Forensics and Security 2025年 20卷 5195-5210页

作者： Wang, Jiacheng Du, Hongyang Liu, Yinqiu Sun, Geng Niyato, Dusit Mao, Shiwen Kim, Dong In Shen, Xuemin Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Jilin University College of Computer Science and Technology 130012 China Auburn University Department of Electrical and Computer Engineering Auburn United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of University of Waterloo Department of Electrical and Computer Engineering Canada

Integrated sensing and communications (ISAC) is one of the crucial technologies for 6G, and channel state information (CSI) based sensing serves as an essential part of ISAC. However, current research on ISAC focuses mainly on improving sensing performance, overlooking security issues, particularly the unauthorized sensing of users. Hence, this paper proposes a diffusion model based secure sensing system (DFSS). Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, which guides the ISAC system to appropriately activate wireless links and nodes, ensuring the sensing performance while minimizing the operation cost. Using the activated links and nodes, DFSS then employs the continuous conditional diffusion model to generate safeguarding signals, which are next modulated onto the pilot at the transmitter to mask fluctuations caused by user activities. As such, only authorized ISAC devices with the safeguarding signals can extract the true CSI for sensing, while unauthorized devices are unable to perform the effective sensing. Experiment results demonstrate that DFSS can reduce the activity recognition accuracy of the unauthorized devices by approximately 70%, effectively shield the user from the illegitimate surveillance. © 2005-2012 IEEE.

关键词： Sensors Wireless communication Diffusion models Wireless sensor networks Communication system security Performance evaluation Security Integrated sensing and communication Fluctuations Accuracy

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A robust image encryption method based on block diagonal chaotic architecture with CNN performance evaluation metric

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Multimedia Tools and Applications 2025年 1-35页

作者： Mamandi, Vida Ardalani, Nahid Ghalamkari, Behbod Department of Electrical Engineering Central Tehran Branch Islamic Azad University Tehran Iran Department of Mechanical Electrical and Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran

In this paper, a new block diagonal chaotic model (BDC) is investigated due to higher necessity of advanced secure data transmission method in wireless medium and considerable limitation on computational storage space. In this model, the image sparse coding using BDC matrix in noisy and Rayleigh fading channels is studied. The structured BDC model is designed with two schemes, including chaotic pseudo inverse matrix (CPI-m) and block diagonal chaotic structured matrix (BDC-m) using two chaotic maps, in which the initial values for generating nonlinear chaotic sequences are used as private key at the receiver side. The simulation results show that the retrieved data measured based on CPI and BDC schemes outperforms the chaotic compressive sensing (CCS) method over AWGN and Rayleigh fading channels by 6% and 22.7%, respectively. Also, the sensitivity of recovered BDC-based image increases to 10(−32) compared to that of 10(−16) for conventional CCS based image. Moreover, the encryption key space increases from 10(37) for standard CCS-based measurements to β×10111 for measured data based on the BDC model, which guarantees that the competitors access no information. Considering datasets from iterative reconstruction process, the image features are extracted using convolutional neural network (CNN) model by ResNet-18 and GoogleNet architectures. The improved performance of data based on CPI and BDC model is validated by retrieved images accuracy over the classifier support vector machine (SVM). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Fading channels

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Optimizing IoT Network Intrusion Detection: A Deep Learning Approach 7

Optimizing IoT Network Intrusion Detection: A Deep Learning ...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Alam, Khorshed Monir, Md Fahad Hassan, Zoheb Habib, Md Tarek Department of Computer Science & Engineering Bangladesh Virginia Tech Department of Electrical and Computer Engineering United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350356717

Network Intrusion Detection System (NIDS) serves as a essential component in data protection by monitoring computer networks for threats that can bypass conventional defenses such as malware and hackers. Deep learning (DL) techniques provide a promising approach for analyzing raw IoT network data to identify subtle patterns indicative of intrusion attempts. This study addresses a crucial research gap by developing a Deep Convolutional Neural Network (DCNN) model specifically designed for the efficient detection of stealthy and polymorphic variants while reducing false positives. Utilizing the NF-ToN-IoT dataset, the proposed model achieves outstanding performance metrics on test data, with an accuracy of 0.9923, precision of 0.9925, recall of 0.9979, and F1 score of 0.9952. To comprehensively evaluate the robustness of the model, a multi-dataset validation strategy is employed. The model is retrained and assessed on established benchmark datasets on IoT Networks, including NF-UNSW-NB15, NF-UNSW-NB15-v2 and NF-BoTIoT, demonstrating exceptional performance. Furthermore, the significance of the contribution is validated by comparing the proposed model against previously established architectures such as CNN+BiLSTM, DNN, GRU+RNN, and CNN+LSTM using the NF-ToN-IoT dataset. The proposed model consistently outperforms these prior models, highlighting its efficacy and advancements in the field. Additionally, an ablation study is conducted to analyze the individual components of the Deep CNN model, providing insights into their contributions towards detecting malware traffic and offering guidance for optimizing future NIDS models in the cybersecurity domain. Making our work available open-source on https://***/codewithkhurshed/IDSIUB can enhance its accessibility and promote future research opportunities in Network Intrusion Detection. © 2024 IEEE.

关键词： Network intrusion

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In-depth review of AI-enabled unmanned aerial vehicles: trends, vision, and challenges

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Discover Artificial Intelligence 2024年第1期4卷 1-24页

作者： Pal, Osim Kumar Shovon, MD Sakib Hossain Mridha, M.F. Shin, Jungpil Department of Electrical & Computer Engineering University of Patras Rion-Patras Patras26504 Greece Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering The University of Aizu Aizu Aizuwakamatsu965-8580 Japan

In recent times, AI and UAV have progressed significantly in several applications. This article analyzes applications of UAV with modern green computing in various sectors. It addresses cutting-edge technologies such as green computing, generative AI, future scope, and related concerns in UAV. The research investigates the role of green computing and generative AI in combination with UAVs for navigation, object recognition and tracking, wildlife monitoring, precision agriculture, rescue operations, surveillance, and UAV communication. This study examines how modern computing technologies and UAVs are being applied in agriculture, surveillance, disaster management, and other areas. The ethics of UAV and AI applications, including safety, legal frameworks, and other issues, are thoroughly investigated. This research examines AI-based UAV applications across different disciplines, using open-source data and current advancements for future growth in this domain. This investigation will aid future researchers in their exploration of UAVs using cutting-edge computing technologies. © The Author(s) 2024.

关键词： Unmanned aerial vehicles (UAV)

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Hybrid two-level protection system for preserving pre-trained DNN models ownership

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Neural Computing and Applications 2024年第34期36卷 21415-21449页

作者： Fkirin, Alaa Moursi, Ahmed Samy Attiya, Gamal El-Sayed, Ayman Shouman, Marwa A. Department of Electrical Engineering Faculty of Engineering Fayoum University Fayoum Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia Governorate Menouf32952 Egypt

Recent advancements in deep neural networks (DNNs) have made them indispensable for numerous commercial applications. These include healthcare systems and self-driving cars. Training DNN models typically demands substantial time, vast datasets and high computational costs. However, these valuable models face significant risks. Attackers can steal and sell pre-trained DNN models for profit. Unauthorised sharing of these models poses a serious threat. Once sold, they can be easily copied and redistributed. Therefore, a well-built pre-trained DNN model is a valuable asset that requires protection. This paper introduces a robust hybrid two-level protection system for safeguarding the ownership of pre-trained DNN models. The first-level employs zero-bit watermarking. The second-level incorporates an adversarial attack as a watermark by using a perturbation technique to embed the watermark. The robustness of the proposed system is evaluated against seven types of attacks. These are Fast Gradient Method Attack, Auto Projected Gradient Descent Attack, Auto Conjugate Gradient Attack, Basic Iterative Method Attack, Momentum Iterative Method Attack, Square Attack and Auto Attack. The proposed two-level protection system withstands all seven attack types. It maintains accuracy and surpasses current state-of-the-art methods. © The Author(s) 2024.

关键词： Deep neural networks

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All-photonic artificial-neural-network processor via nonlinear optics

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Physical Review Applied 2024年第1期22卷 014009-014009页

作者： Jasvith Raj Basani Mikkel Heuck Dirk R. Englund Stefan Krastanov Department of Electrical and Electronics Engineering Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics and Department of Electrical Engineering and Computer Science Department of Electrical and Photonics Engineering College of Information and Computer Sciences

Optics and photonics have recently captured interest as a platform to accelerate linear matrix processing, otherwise a bottleneck in traditional digital electronics. In this paper we propose an all-photonic computational accelerator wherein information is encoded in the amplitudes of frequency modes stored in a single ring resonator. Interaction among these modes is enabled by nonlinear optical processes. Both the matrix multiplication and elementwise activation functions on these modes (the artificial neurons) are performed through coherent processes, enabling the direct representation of negative and complex numbers without having to pass through digital electronics, a common limitation in today’s photonic architectures. This design also has a drastically lower hardware footprint compared with today’s electronic and optical accelerators, as the entirety of the matrix multiplication happens in a single multimode resonator on chip. Our architecture is unique in providing a completely unitary, reversible mode of computation, enabling on-chip analog Hamiltonian-echo backpropagation for gradient descent and other self-learning tasks. Moreover, the computational speed increases with the power of the pumps to arbitrarily high rates, as long as the circuitry can sustain the higher optical power. Lastly, the design presented here is a less demanding version of a future room-temperature quantum computational device. Therefore, while this architecture is already viable today, direct reinvestments in it would be enabling its evolution into quantum computational hardware.

关键词： Machine learning Neuromorphic computing Nonlinear optics photonics Artificial neural networks Deep learning Neural network simulations Optical computing

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Hybrid Kalman Filter and Optimization-Based Routing for Energy Efficiency in Heterogeneous Wireless Sensor Networks

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Informatica (Slovenia) 2024年第23期48卷 121-132页

作者： Marzook, Ali K. Alkenani, Jawad University of Basrah College of Engineering Department of Electrical Engineering Iraq Department of Computer Science Shatt Al-Arab University College Basra Iraq

Several significant research studies have been done in distributed applications, database management systems, and information collecting in computer science concerning data mining and processing for wireless sensor networks (WSNs). As a result of WSNs' limited computation, networking, and data mining capabilities, the primary objective of creating WSN-based applications has proven to be extremely challenging: making decisions in real time. As such, typical data mining techniques are difficult to apply to sensor data due to their nature, peculiarities, and the constraints of wireless sensor networks. This work introduces a novel method for data mining and gathering and noise removal in wireless sensor networks (WSNs), dubbed KF-BA. This methodology increases network lifetime and efficiency by combining the Kalman Filter (KF) with the Bat Algorithm (BA). The recommended methods and BA contrast the provided techniques for raising energy consumption and prolonging network lifetime. The BA algorithm is not as effective as the KF-BA approach. The suggested strategy yields network longevity that is almost (57%) longer than BA in this instance, and that is after 2,000 packets are sent to two sensors spread over the network. The results were simulated using the MATLAB environment. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Kalman filters

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Consensus-relevance kNN and covariate shift mitigation

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MACHINE LEARNING 2024年第1期113卷 325-353页

作者： Kalpakis, Konstantinos Computer Science and Electrical Engineering Department University of Maryland Baltimore County 1000 Hilltop Circle Baltimore MD 21250 USA

Classification and regression algorithms based on k-nearest neighbors (kNN) are often ranked among the top-10 Machine learning algorithms, due to their performance, flexibility, interpretability, non-parametric nature, and computational efficiency. Nevertheless, in existing kNN algorithms, the kNN radius, which plays a major role in the quality of kNN estimates, is independent of any weights associated with the training samples in a kNN-neighborhood. This omission, besides limiting the performance and flexibility of kNN, causes difficulties in correcting for covariate shift (e.g., selection bias) in the training data, taking advantage of unlabeled data, domain adaptation and transfer learning. We propose a new weighted kNN algorithm that, given training samples, each associated with two weights, called consensus and relevance (which may depend on the query on hand as well), and a request for an estimate of the posterior at a query, works as follows. First, it determines the kNN neighborhood as the training samples within the kth relevance-weighted order statistic of the distances of the training samples from the query. Second, it uses the training samples in this neighborhood to produce the desired estimate of the posterior (output label or value) via consensus-weighted aggregation as in existing kNN rules. Furthermore, we show that kNN algorithms are affected by covariate shift, and that the commonly used sample reweighing technique does not correct covariate shift in existing kNN algorithms. We then show how to mitigate covariate shift in kNN decision rules by using instead our proposed consensus-relevance kNN algorithm with relevance weights determined by the amount of covariate shift (e.g., the ratio of sample probability densities before and after the shift). Finally, we provide experimental results, using 197 real datasets, demonstrating that the proposed approach is slightly better (in terms of F-1 score) on average than competing benchmark approaches for mit

关键词： Nearest neighbors Selection bias Covariate shift Density estimation kNN learners

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in electrical Networks

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computer Modeling in engineering & sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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Correction to: Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images (Physical and engineering sciences in Medicine, (2024), 10.1007/s13246-024-01420-1)

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Physical and engineering sciences in Medicine 2025年第1期48卷 19-20页

作者： Mostafavi, Mohammadreza Ko, Seok-Bum Shokouhi, Shahriar Baradaran Ayatollahi, Ahmad Department of Electrical Engineering Electronic Engineering Iran University of Science and Technology Tehran Iran Division of Biomedical Engineering Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon Canada

In this article the legend of Fig. 6 was presented without a reference. The legend of Fig. 6 has been changed from "The general framework for knowledge distillation involving a teacher-student relationship" to "The generic teacher-student framework for knowledge distillation [33]." In Sharma et al.’s research, two results obtained from the "KNN with ten-fold cross-validation" and "KNN with leave-one-subject-out cross-validation (LOSOCV)" methods are reported as the final results. Unfortunately, we mistakenly attributed the accuracy of one method. While we correctly listed the name of the method used, which is KNN with ten-fold cross-validation, we inadvertently entered the precision of the KNN with leave-one-subject-out cross-validation (LOSOCV) method. Therefore, we updated our results table (Table 7). The correct Table 7 is shown below. (Table presented.) Comparison of the performance of the proposed method with existing methods Study Method Dataset Accuracy Sensitivity Specificity Shim et al. [38] SVM 34 SZ-34 healthy 88.24% – – Siuly et al. [7] EBT – EMD (Empirical Mode Decomposition) 49 SZ-32 healthy 89.59% 0.8976 0.8932 Phang et al. [39] Multi-domain connectome CNN 45 SZ-39 healthy 91.69% 0.9111 0.925 Jahmunah et al. [40] Non-linear features, decision-tree, neural network, SVM 14 SZ and 14 healthy 92.90% 0.9345 0.9224 Akbari et al. [41] Phase space dynamic & graphical features 14 SZ and 14 healthy 94.8% 0.943 0.952 Shalbaf et al. [20] ResNet-18-SVM 14 SZ and 14 healthy 94.84% 0.980 0.9239 Bagherzadeh et al. [42] DenseNet121 model 14 SZ and 14 healthy 96.26% 0.9548 0.9702 Sharma et al. [43] KNN with ten-fold 14 SZ and 14 healthy 99.21% 0.9942 0.9905 LOSOCV 97.20% 0.9649 0.9806 Proposed method CWT—Self distillation- VGG16 14 SZ and 14 healthy 97.81 0.970 0.986 The original article has been corrected. © Australasian College of Physical Scientists and Engineers in Medicine 2024.

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