Enhancing covert communication in NOMA systems with joint security and covert design

PLoS ONE, Journal Year: 2025, Volume and Issue: 20(1), P. e0317289 - e0317289

Published: Jan. 13, 2025

The explosion of Internet-of-Thing enables several interconnected devices but also gives rise chance for unauthorized parties to compromise sensitive information through wireless communication systems. Covert therefore has emerged as a potential candidate ensuring data privacy in conjunction with physical layer transmission render two lines defense. In this paper, we aim enhance the individual nearby users non-orthogonal multiple access (NOMA) systems under scenarios an eavesdropper who monitors covert before decoding information. For problem, first provide comprehensive analysis NOMA system terms outage probability (OP), secrecy (SOP), and detection error (DEP), where all them are quantified exact asymptotic closed-form expressions. Besides, have derived formulas users’ public rates. Under requirements maximal OP SOP minimal DEP, formulate optimization resource power allocation to: 1) minimize 2) maximize rate. Thanks developed analytical expressions, obtain expressions sub-optimal coefficient each problem. Simulation results validate efficacy mathematical frameworks reveal that proposed approaches can attractive performance improvement compared fixed allocations only.

Language: Английский

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Statistical Analysis of the Sum of Double Random Variables for Security Applications in RIS-Assisted NOMA Networks with a Direct Link

Electronics, Journal Year: 2025, Volume and Issue: 14(2), P. 392 - 392

Published: Jan. 20, 2025

Next- generation wireless communications are projected to integrate reconfigurable intelligent surfaces (RISs) perpetrate enhanced spectral and energy efficiencies. To quantify the performance of RIS-aided networks, statistics a single random variable plus sum double variables becomes core approach reflect how communication links from RISs improve wireless-based systems versus direct ones. With this in mind, work applies secure RIS-based non-orthogonal multi-access (NOMA) with presence untrusted users. We propose new strategy by jointly considering NOMA encoding RIS’s phase shift design enhance legitimate nodes while degrading channel capacity elements but sufficient power resources for signal recovery. Following that, we analyze derive closed-form expressions secrecy effective (SEC) outage probability (SOP). All analyses supported extensive Monte Carlo simulation outcomes, which facilitate an understanding system behavior, such as transmit signal-to-noise ratio, number RIS elements, allocation coefficients, target data rate channels, rate. Finally, results demonstrate that our proposed can be improved significantly increase irrespective proximate or distant

Language: Английский

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<i>k</i>-anonymity in Resource Allocation for Vehicle-to-Everything (V2X) Systems

Journal of Telecommunications and Information Technology, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 4

Published: Feb. 10, 2025

Sixth generation (6G) vehicle-to-everything (V2X) systems face numerous security threats, including Sybil and denial-of-service (DoS) cyber-attacks. To provide a secure exchange of data protect users' identities in 6G V2X communication systems, anonymization techniques - such as k-anonymity can be used. In this work, we study centralized vs. based resource allocation methods vehicular edge computing (VEC) network. Allocation decisions for networks are classically posed optimization task. Therefore, an information flow is transmitted from the vehicles to premises. addition decision, vehicle not required. We analyze versus k-anonymous models. show potential deterioration introduced by anonymity, quantify gap optimal goal two cases: on with aim at energy reduction. Our numerical results indicate that consumption rises 1% smaller scenarios 23% medium scenarios, whereas it decreases 14% larger scenarios.

Language: Английский

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A critical review of waste tire pyrolysis for diesel engines: Technologies, challenges, and future prospects

Sustainable materials and technologies, Journal Year: 2025, Volume and Issue: unknown, P. e01291 - e01291

Published: Feb. 1, 2025

Language: Английский

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A security enhancement analysis of full duplex cooperative rate-splitting

Physical Communication, Journal Year: 2025, Volume and Issue: unknown, P. 102665 - 102665

Published: March 1, 2025

Language: Английский

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Recent Developments in IoT Security and Privacy: A Review of Best Practices with Challenges and Emerging Solutions

Published: April 3, 2025

The Internet of Things has changed many fields by making it easy for smart devices to talk each other. On the other hand, this change led major security and privacy problems, such as malware attacks, unauthorized access, data leaks, weak authentication systems. IoT gadgets are targets hackers because they often don't have a lot processing power. Additionally, massive generated raises concerns regarding surveillance misuse. Traditional measures insufficient ecosystems, necessitating innovative solutions. Emerging approaches include blockchain decentralized security, AI-driven anomaly detection, lightweight encryption techniques, zero-trust architectures. Regulatory frameworks technologies that protect privacy, federated learning differential also becoming more common. Even with these changes, it's still hard find good balance between usability. It talks about newest threats in well fresh methods strengthen environment.

Language: Английский

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Predicting Urban Traffic Congestion with VANET Data

Computation, Journal Year: 2025, Volume and Issue: 13(4), P. 92 - 92

Published: April 7, 2025

The purpose of this study lies in developing a comparison neural network-based models for vehicular congestion prediction, with the aim improving urban mobility and mitigating negative effects associated traffic, such as accidents congestion. This research focuses on evaluating effectiveness different network architectures, specifically Transformer LSTM, order to achieve accurate reliable predictions To carry out research, rigorous methodology was employed that included systematic literature review based PRISMA methodology, which allowed identification synthesis most relevant advances field. Likewise, Design Science Research (DSR) applied guide development validation models, CRISP-DM (Cross-Industry Standard Process Data Mining) used structure process, from understanding problem implementing solutions. dataset key variables related vehicle speed, flow, weather conditions. These were processed normalized train evaluate various highlighting LSTM networks. results obtained demonstrated LSTM-based model outperformed task prediction. Specifically, achieved an accuracy 0.9463, additional metrics loss 0.21, 0.93, precision 0.29, recall 0.71, F1-score 0.42, MSE 0.07, RMSE 0.26. In conclusion, demonstrates is highly effective predicting congestion, surpassing other architectures Transformer. integration into simulation environment showed real-time traffic information can significantly improve management. findings support utility sustainable planning intelligent management, opening new perspectives future

Language: Английский

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Peer-driven task scheduling and resource allocation for enhanced performance in industrial IoT systems

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 25, 2025

Language: Английский

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Neural Network-Driven Smart City Security Monitoring in Beijing Multimodal Data Integration and Real-Time Anomaly Detection

International Journal of Computer Science and Information Technology, Journal Year: 2024, Volume and Issue: 3(3), P. 91 - 102

Published: Aug. 12, 2024

Our study presents the development and implementation of a neural network-based smart city security monitoring system tailored for urban environment Beijing. Leveraging multimodal data integration, processes over 1,200 hours video footage, 800 audio recordings, 400 thermal to provide comprehensive surveillance real-time anomaly detection. The achieved high accuracy rates 96% overcrowding detection, 93% unauthorized access, 90% unattended objects, with corresponding precision 96%, 95%, 93%. recall were slightly lower, at 89%, 87%, 85%, respectively. system's edge computing enabled rapid response times, recorded 1.5 seconds subway stations, 2.0 Tiananmen Square, 1.2 public transport hubs. These results underscore effectiveness in delivering timely alerts, crucial managing high-density areas critical infrastructure integration advanced AI techniques, including transfer learning Generative Adversarial Networks (GANs), further enhanced adaptability robustness detecting rare unlabeled events. This highlights potential significantly improve infrastructure, offering scalable efficient solution applications.

Language: Английский

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Enhancing IoT Security Using GA-HDLAD: A Hybrid Deep Learning Approach for Anomaly Detection

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(21), P. 9848 - 9848

Published: Oct. 28, 2024

The adoption and use of the Internet Things (IoT) have increased rapidly over recent years, cyber threats in IoT devices also become more common. Thus, development a system that can effectively identify malicious attacks reduce security has topic great importance. One most serious comes from botnets, which commonly attack by interrupting networks required for to run. There are number methods be used improve identifying unknown patterns networks, including deep learning machine approaches. In this study, an algorithm named genetic with hybrid learning-based anomaly detection (GA-HDLAD) is developed, aim improving botnets within environment. GA-HDLAD technique addresses problem high dimensionality using during feature selection. Hybrid detect botnets; approach combination recurrent neural (RNNs), extraction techniques (FETs), attention concepts. Botnet involve complex (HDL) method detect. Moreover, FETs model ensures features extracted spatial data, while temporal dependencies captured RNNs. Simulated annealing (SA) utilized select hyperparameters necessary HDL approach. experimentally assessed benchmark botnet dataset, findings reveal provides superior results comparison existing methods.

Language: Английский

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