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Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks

Alexandria engineering Journal 2025年 128卷 127-143页

作者： Islam, Shayla Topazal, S.M. Hasan, Mohammad Kamrul Kolandaisamy, Raenu AL Abbas, Huda Saleh Vo, Nguyen Thakkar, Rahul Khan, Muhammad Attique Gupta, Deepak AlHammadi, Dina Abdulaziz Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Department of computer science College of Computer Science and Engineering Taibah University Madina Saudi Arabia Victorian Institute of Technology Melbourne Australia Center of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Maharaja Agrasen Institute of Technology Delhi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia

The emergence of 5 G and 6 G networks promises significant advancements in the Internet of Medical Things (IoMT) by enabling real-time medical data transmission and continuous connectivity for mobile healthcare devices. However, traditional Handover (HO) mechanisms face latency, security, energy consumption, and scalability challenges when supporting fast-moving and dense IoMT environments. This paper presents a secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks, incorporating a predictive algorithm, Blockchain-Based (BC-B) decentralized authentication mechanism, and AI-driven anomaly detection. The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. Meanwhile, blockchain-based authentication ensures tamper-proof and decentralized security, reducing vulnerability to cyberattacks by 90 % compared to traditional methods. Additionally, the energy consumption during HO events is reduced by 20–35 %, dropping from 15 mJ in traditional schemes to 12 mJ in 5 G and further to 10 mJ in 6 G. The scheme also incorporates AI-driven anomaly detection, achieving an accuracy of 96.7 %, ensuring proactive identification and mitigation of potential security threats. The analyzed scheme maintains consistent throughput with less than 5 % reduction and scales effectively for up to 300 devices per cell. The simulation results demonstrate significant improvements across key performance metrics, including latency, security, energy efficiency, and scalability, making this approach highly suitable for critical IoMT applications such as telesurgery, remote patient monitoring, and innovative healthcare environments. These advancements position the analyzed framework as a cornerstone for next-generation (NG) healthcare networks. © 2025 The Authors

关键词： Remote patient monitoring

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Performance Analysis of Three Spectrum Sensing Detection Techniques with Ambient Backscatter Communication in Cognitive Radio Networks

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computer Modeling in engineering & sciences 2023年第10期137卷 813-825页

作者： Shayla Islam Anil Kumar Budati Mohammad Kamrul Hasan Saoucene Mahfoudh Syed Bilal Hussian Shah Institute of Computer Science&Digital Innovation ICSDIUCSI UniversityKuala Lumpur56000Malaysia Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)Selangor43600Malaysia School of Engineering Computing and InformaticsDar Al-Hekma UniversityJeddah22246Saudi Arabia

In wireless communications, the Ambient Backscatter Communication (AmBC) technique is a promisingapproach, detecting user presence accurately at low power levels. At low power or a low Signal-to-Noise Ratio(SNR), there is no dedicated power for the users. Instead, they can transmit information by reflecting the ambientRadio Frequency (RF) signals in the spectrum. Therefore, it is essential to detect user presence in the spectrum forthe transmission of data without loss or without collision at a specific time. In this paper, the authors proposed anovel Spectrum Sensing (SS) detection technique in the Cognitive Radio (CR) spectrum, by developing the *** Matched Filter Detection with Inverse covariance (MFDI), Cyclostationary Feature Detection with Inversecovariance (CFDI) and Hybrid Filter Detection with Inverse covariance (HFDI) approaches are used with AmBCto detect the presence of users at low power levels. The performance of the three detection techniques is measuredusing the parameters of Probability of Detection (PD), Probability of False Alarms (Pfa), Probability of MissedDetection (Pmd), sensing time and throughput at low power or low SNR. The results show that there is a significantimprovement via the HFDI technique for all the parameters.

关键词： Ambient backscatter communication cognitive radio MFDI CFDI HFDI

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Automated Crop Growth Monitoring and Optimizing the Yield with Reinforcement Learning 3

Automated Crop Growth Monitoring and Optimizing the Yield wi...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Gorantla, Snehitha Veluchamy, S. Amrita School of Computing Department of Computer Science and Engineering - Artificial Intelligence Amrita Vishwa Vidyapeetham Chennai601103 India Amrita School of Computing Department of Computer Science and Engineering - Cyber Security Amrita Vishwa Vidyapeetham Chennai601103 India

ISBN: (纸本)9798350317022

A reinforcement learning agent for optimal green-house management through Proximal Policy Optimisation is developed and evaluated in this work. With variable elements like lighting, irrigation, humidity, and temperature, a virtual greenhouse is produced. By interacting with the surroundings, the agent modifies the controls and gets input on things like crop health and growth rate. For policy and value estimation, the neural network design consists of distinct actor and critic networks as well as shared feature layers. Compared to fixed or random policies, the trained agent shows the ability to regulate conditions for increased crop yield and sustainability. As the number of trials increased, the RL agent's average return per trial increased steadily. This suggests that the RL agent has become more adept at controlling the greenhouse environment in subsequent experiments. This demonstrates how well the RL agent can manage crop health and resource use in the greenhouse climate control system. © 2023 IEEE.

关键词： Crop Yield Greenhouse Neural Network PPO algorithm Reinforcement Learning

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Decrease the Prompt Uncertainty: Adversarial Prompt Learning for Few-shot Text Classification

Decrease the Prompt Uncertainty: Adversarial Prompt Learning...

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

作者： Weng, Jinta Zhang, Zhaoguang Yaqi, Jing Niu, Chenxu Huang, Heyan Hu, Yue School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Cyber Engineering Guangzhou University China National Computer Network Emergency Response Technical Team Coordination Center of China China Beijing Institute of Technology School of Computer Science and Technology Beijing China Southeast Academy of Information Technology Putian China

ISBN: (纸本)9781665410205

With few-shot learning abilities, pre-trained language models (PLMs) have achieved remarkable success in classification tasks. However, recent studies have shown that the performance of PLM is vulnerable due to different prompts and the instability of the prompt-based learning process. To address this challenge, we explore appropriate perturbation addition of adversarial training and integrate the global knowledge of the full-parameter fine-tuned pre-trained language model(PLM). Specifically, we propose an adversarial prompt learning model (ATPET) and ATPET with fine-tuning(ATPET-FT), incorporating ATPET with fine-tuning knowledge into the prompt learning process. Through extensive experiments on several few-shot classification tasks and challenging data settings, we demonstrate that our methods consistently improve the robustness while maintaining the effectiveness of PLMs. © 2024 IEEE.

关键词： Adversarial machine learning

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Comparison of Multiple Regression Particle Swarm Optimization Algorithms and Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization for Host Overload / Under-Loaded Detection in Cloud Datacenter 3

Comparison of Multiple Regression Particle Swarm Optimizatio...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Aqlan Alhammadi, Akram Saeed Raman, Jayakrishnan Anilakkad Mosleh, Mohammed A.S. Ali Abdullah, Hesham Mohammed It College Taiz University Aljanad University Alsaeed Faculty Engineering Engineering of Computer Networkand Cyber Security Department Taiz Yemen Cyber Forensic Consultant CyberSec Analytics Kerala India Al-Fayha College Jubail Industrial City Department of Computer Science and Engendering Saudi Arabia Jouf University Department of Computer Science and It Sakaka Saudi Arabia

ISBN: (纸本)9798350305333

Power consumption of the data centers in recent times is a big challenge presently. Virtualization plays a key role in improving efficacy. Virtual Machine consolidation (VMC) is a technique for efficient usage of a resource to reduce energy consumption by consolidating Virtual Machines (VMs) into fewer physical machines without Service level agreement violation (SLAV). In this paper, we compare two algorithms called Multiple Regression Particle Swarm Optimization (MRPSO) Algorithm and Multiple Regression Multi-Objective Seven-Spot Ladybird Optimization (MR-MOSLO) Algorithm for host overloaded and under loaded detection. The comparison result shows that the performance of MRPSO is better to MR-MOSLO when the number of VMs is few. © 2023 IEEE.

关键词： Virtual machine

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An Integrated Platform for Virtual Ecosystems and Wireless Edge Computing for Web 3.0 3

An Integrated Platform for Virtual Ecosystems and Wireless E...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Garg, Khushi Jattu, Mahee Hariharasitaraman, S. Subramanian, R. Raja Division of Cyber Security and Digital Forensics VIT Bhopal University Madhya Pradesh India School of Computing Science and Engineering Vit Bhopal University Madhya Pradesh India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India

ISBN: (纸本)9798350361186

The emergence of Web 3.0 has paved the way for a new generation of the internet that enables users to view, publish, and own content independently. This innovation is being driven by a combination of blockchain, semantic communication, edge computing, and artificial intelligence, which together create value networks that facilitate participatory decision-making. Blockchain, in particular, offers security services by recording content in a decentralized and transparent manner. Additionally, it is compatible with decentralized wireless edge computing designs that can accurately express the intended meanings of contents without requiring a lot of resources. Building on this foundation, this research proposes a paradigm for unified blockchain-semantic ecosystems for Web 3.0 with wireless edge intelligence. Specifically, we present a semantic proof technique built on Oracle that enables Web 3.0's off-chain and on-chain interactions. To improve interaction efficiency and support Web 3.0, Oracle has developed an adaptable Deep Reinforcement Learning-based sharding technique. Our work also introduces a blockchain-based semantic exchange architecture that tokenizes semantic data into non-fungible tokens (NFT) for semantic exchange in this system. We employ Zero-Knowledge Proof to trade real semantic information without publicly posting it before accepting payment, in contrast to traditional NFT marketplaces. This makes it possible to trade fairly and privately. Overall, our proposed framework can precisely perform decentralized semantic sharing and information transfer, further achieving the advantages of semantic extraction and communication in Web 3.0. © 2024 IEEE.

关键词： Edge computing

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FTGraph: A Flexible Tree-Based Graph Store on Persistent Memory for Large-Scale Dynamic Graphs

FTGraph: A Flexible Tree-Based Graph Store on Persistent Mem...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Sun, Gan Zhou, Jiang Li, Bo Gu, Xiaoyan Wang, Weiping He, Shuibing Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Cyberspace Security Defense Beijing China College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (纸本)9798350358711

Traditional in-memory graph systems often suffer from scalability due to the limited capacity and volatility of DRAM. Emerging non-volatile memory (NVM) provides an opportunity to achieve highly scalable and high-performance graph stores for its large capacity and persistence characteristics. However, directly deploying current in-memory graph storage systems on NVM would cause significant inefficiencies in NVM access, as their graph organization designed for DRAM may incur higher write amplification, crash inconsistency and costly concurrency control overhead in NVM for write-intensive work-loads. In this paper, we propose FTGraph, a Flexible Tree-based Graph storage system, for both efficient dynamical graph updates and analysis. To achieve this goal, we introduce a novel degree-aware suffix bit tree to effectively manage vertices and edges of the graph, enabling adaptability to real-world power-law degree distributions while significantly reducing NVM writes. Based on it, we adopt two optimization methods, logical vertex ID translation and sequential storage, for vertices with very high degrees within the tree to enhance graph analysis operations. We further integrate 8B NVM atomic writes with optimistic version-based concurrency control through a dual bitmap design to ensure low-overhead, log-free crash consistency and reduce read-writer contention. Experimental results show that FTGraph achieves up to 21.2× higher update performance and up to 85.4 × higher analysis performance, compared with state-of-the-art dynamic graph systems implemented on NVMs. © 2024 IEEE.

关键词： Concurrency control

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Natural Language Interface for Data Visualization: Harnessing the Power of Large Language Models

Natural Language Interface for Data Visualization: Harnessin...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Buchepalli Praneeth Ashutosh Kumar Singh Jyothika K Raju Mohana B Suma Computer Science and Engineering RV College of Engineering Bengaluru India Computer Science and Engineering (Cyber Security) RV College of Engineering Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Democratizing data analysis is the need of the hour, empowering a wider range of users within corporations to engage with data and uncover valuable insights. Because of the inherent ambiguity of natural language and the frequent lack of quality and clarity in user queries, implementing functioning Natural Language Interfaces (NLIs) has proven to be a challenging task. These problems make it difficult for existing language models to understand user intent effectively. This study introduces a novel data visualization tool that leverages the power of Large Language Models (LLMs) to empower corporations and research institutions in extracting meaningful insights from vast datasets. LLMs interpret user input and generate goals, which LIDA, a library developed by Microsoft, uses to create a wide range of visualizations with libraries like matplotlib, seaborn, and plotly, based on these goals and user instructions. This system bridges the gap between Natural Language Interfaces(NLIs) and data visualization by enabling users to submit prompts in natural language. Users can formulate questions in natural language, eliminating the need for complex code or data manipulation expertise. This work compares and contrasts the performance of open source models Mistral 7B instruct-v3, LLaMa3 8B, and proprietary model, gpt 3.5-turbo across several case studies as the graphs generated are dependent completely on the interpretation power of LLMs.

关键词： Visualization Data analysis Large language models Computational modeling Natural languages Data visualization Libraries Robustness Data models Data mining

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EVADE: Targeted Adversarial False Data Injection Attacks for State Estimation in Smart Grid

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年第3期10卷 534-546页

作者： Tian, Jiwei Shen, Chao Wang, Buhong Ren, Chao Xia, Xiaofang Dong, Runze Cheng, Tianhao Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an710049 China Air Force Engineering University Air Traffic Control and Navigation College Xi’an710051 China Air Force Engineering University Information and Navigation College Xi’an710043 China Nanyang Technological University School of Electrical and Electronic Engineering 639798 Singapore Xidian University School of Computer Science and Technology Xi’an710126 China

Although conventional false data injection attacks can circumvent the detection of bad data detection (BDD) in sustainable power grid cyber physical systems, they are easily detected by well-trained deep learning-based detectors. Still, state estimation models with deep leaning-based detectors are not secure due to the vulnerabilities and fragility of deep learning models. Using the related laws of conventional false data injection attacks and adversarial sample attacks, this paper proposes the targEted adVersarial fAlse Data injEction (EVADE) strategy to explore targeted adversarial false data injection attacks for state estimation in Smart Grid. The proposed EVADE attack strategy selects key state variables based on adversarial saliency maps to improve the attack efficiency and perturbs as few state variables as possible to reduce the attack cost. In this way, the EVADE attack strategy can bypass the detection of BDD and neural attack detection (NAD) methods (that is, maintaining deep stealthy) with a high success rate and achieve the attack target simultaneously. Experimental results demonstrate the effectiveness of the proposed strategy, posing serious and pressing concerns for sustainable cyber physical power system security. © 2016 IEEE.

关键词： cyber attacks

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Senspy: An Integrated Network Monitoring and Anomaly Detection System Using Machine Learning

Senspy: An Integrated Network Monitoring and Anomaly Detecti...

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Bandaru Jnyanadeep Nikunj Mittal Srivishnu P N Varshith Y Mohana Minal Moharir Ashok Kumar A R Computer Science and Engineering (Cyber Security) RV College of Engineering® Bengaluru India Computer Science and Engineering RV College of Engineering® Bengaluru India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

In the evolving cybersecurity landscape, the surge in cyber threats necessitates innovative defence mechanisms. Proposed work explores the integration of ML methods into Intrusion Detection System (IDS) Senspy tool. Senspy, an adaptive IDS software incorporates ML techniques like k-Nearest Neighbors (KNN), Random Forest and Logistic Regression, to enhance its detection capabilities of cyber threats. It's beneficial for recognizing outliers or unusual patterns in network traffic. By employing ML techniques, Senspy excels in detecting and identifying various cyber-attacks, surpassing traditional IDS capabilities. The system demonstrates robust performance, particularly with Random Forest algorithm achieving over accuracy and an F1 score of 99.6%.

关键词： Logistic regression Machine learning algorithms Accuracy Telecommunication traffic Nearest neighbor methods Prediction algorithms Windows

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