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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Kurra, Sai Suchith Rayana, Likhitha Yoosuf, Mohamed Sirajudeen School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Department of Mathematics and Computer Science Modern College of Business and Science Muscat Oman

ISBN: (纸本)9789819751990

With the increasing complexity of the cyber-attacks, traditional intrusion systems have become inadequate in effectively detecting and preventing network attacks. Modern machine learning techniques have the capability of preventing sophisticated and aggressive cyber-attacks and have emerged as a promising approach to enhance the accuracy and efficiency of the intrusion detection. This research paper provides a comprehensive analysis and evaluation of the integration of machine learning and deep learning algorithms into intrusion detection system with respect to the Distributed Denial of Service (DDoS) attack. The study begins by presenting an overview of intrusion detection systems, highlighting their importance and significance in safeguarding network infrastructure. Subsequently, it explores supervised machine learning algorithms and deep learning models, such as Random Forest tree, Support Vector Machines (SVM), ResNet and TabNet. The paper presents the pros and cons of existing IDS models with respect to real-time processing, scalability, false positive and false negative results, and IDS vulnerabilities. The above-mentioned algorithms have been trained and evaluated with CICIDS-2017 dataset. The performance metrics used to evaluate the IDS models are accuracy, precision, F1 score, training time of each model, and adaptability to evolving attack patterns. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Network intrusion

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes

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Digital Communications and Networks 2023年第4期9卷 906-919页

作者： Yizhou Shen Shigen Shen Qi Li Haiping Zhou Zongda Wu Youyang Qu Department of Computer Science and Engineering Shaoxing UniversityShaoxing312000China School of Computer Science and Informatics Cardiff UniversityCardiffCF243AAUnited Kingdom School of Information Engineering Huzhou UniversityHuzhou 313000ZhejiangChina School of Information Technology Deakin UniversityBurwoodVIC3125Australia

The fast proliferation of edge devices for the Internet of Things(IoT)has led to massive volumes of data *** generated data is collected and shared using edge-based IoT structures at a considerably high ***,the data-sharing privacy exposure issue is increasingly intimidating when IoT devices make malicious requests for filching sensitive information from a cloud storage system through edge *** address the identified issue,we present evolutionary privacy preservation learning strategies for an edge computing-based IoT data sharing *** particular,we introduce evolutionary game theory and construct a payoff matrix to symbolize intercommunication between IoT devices and edge nodes,where IoT devices and edge nodes are two parties of the *** devices may make malicious requests to achieve their goals of stealing ***,edge nodes should deny malicious IoT device requests to prevent IoT data from being *** dynamically adjust their own strategies according to the opponent's strategy and finally maximize the *** upon a developed application framework to illustrate the concrete data sharing architecture,a novel algorithm is proposed that can derive the optimal evolutionary learning ***,we numerically simulate evolutionarily stable strategies,and the final results experimentally verify the correctness of the IoT data sharing privacy preservation ***,the proposed model can effectively defeat malicious invasion and protect sensitive information from leaking when IoT data is shared.

关键词： Privacy preservation Internet of things Evolutionary game Data sharing Edge computing

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A Simple Algorithm to Secure Data Dissemination in Wireless Sensor Network 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Sonai, Veeramani Bharathi, Indira Department of Computer Science and Engineering School of Engineering Shiv Nadar University Chennai Tamilnadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Chennai India

ISBN: (数字)9789819981298

ISBN: (纸本)9789819981281

Wireless sensor networks are heavily utilized in numerous applications in both industry and science. As the original data is transferred from the sensor node to the sink node, unauthorized individuals attempt to access it, breaching confidentiality. The first step in enhancing the security of data transmission in a wireless sensor network (WSN) is figuring out the best method for encrypting data as it moves from the sensors to the central node. In order to move encrypted data from different sensor nodes to sink nodes efficiently in wireless sensor networks, this paper introduces a simple data encryption technique. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cryptography

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A nonlinear recurrent encoders for early detection of strep throat infection to prevent acute rheumatic fever

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2201-2213页

作者： Kumar, K. Antony Belinda, M. J. Carmel Mary Kumar, V. Dhilip Rajan, Jerlin Francy Arif, Muhammad Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Medical Surgical Nursing Dr. Jeyashekaran Medical Trust Nagercoil India Superior University Lahore Pakistan

Rheumatic Fever or Acute Rheumatic Fever (RF) and Rheumatic Heart Disease (RHD) are commonly suffered by people of all ages around the world. RF usually begins with strep throat infections, skin problems, joint pain etc. Clinical tests and medical informatics are useful to save patients from Streptococcus Group-A bacteria using medical treatments and detection mechanism. Clinical data based statistical analysis approaches assist medical diagnosis model in detecting diseases based on standard decision. Standard decision making frameworks are effective when using these approaches. Nevertheless, existing mechanisms cannot detect or predict the incidence of strep throat infection at an earlier stage based on unclear datasets or complex datasets. The recent computational healthcare informatics systems use Machine Learning (ML) and Deep Learning (DL) techniques, but they are not sufficient in terms of their field integration. The proposed work implements a nonlinear recurrent auto encoder using complex data evaluation procedures to accurately detect and predict the strep throat infections accurately. In this regard, the proposed model builds on the technical features of Denoised Variational Stacked Auto Encoders (DVSE), nonlinear regression computations and Long Shot Term Memory (LSTM) to perform recurrent data analysis practices. Compared with the existing techniques such as Blood Serum Content Analysis model (BSCA), Smartphone based Strep Throat Detection (SSTD) approach and Logistic Regression Based RF Detection approach (LRRF), the multi-level medical data evaluation (throat images and blood samples) provides 10–15% more accuracy for strep throat detection. As shown in the implementation section, the proposed nonlinear recurrent system has the potential to detect strep throat infections early. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Diagnosis

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Combined Multi-Agent and Centralized Resource Allocation in Cloud Radio Access Networks

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IEEE Access 2025年 13卷 79098-79106页

作者： Rodoshi, Rehenuma Tasnim Nazib, Rezoan Ahmed Chun, Chanjun Choi, Wooyeol George Mason University Department of Computer Science Fairfax61452 United States Chosun University Department of Computer Engineering Gwangju61452 Korea Republic of Chung-Ang University School of Computer Science and Engineering Seoul06974 Korea Republic of

The fifth generation (5G) mobile network is designed to facilitate high data rates with massive connectivity with the benefit of small cell technology. The cloud radio access network (C-RAN) is a promising mobile network architecture that can meet the ever-increasing resource demand of a growing number of users. In C-RAN, base station functionalities are separated into baseband units (BBUs) and remote radio heads (RRHs), with BBUs centralized and virtualized via cloud computing. However, this architecture introduces new challenges in efficiently allocating resources to dynamic users. This paper aims to design a resource allocation scheme that improves system efficiency and satisfies dynamic user demands in C-RAN. We propose a hybrid resource allocation approach that combines centralized control and multi-agent-based decision-making. The centralized controller, located within the BBU pool, collaborates with virtual base stations (VBSs) acting as multi-agent system (MAS) agents. The resource allocation solution is derived by jointly considering the real-time resource requests from agents and the historical demand estimates generated by the centralized controller. Through simulation-based evaluation, we compare our proposed scheme with conventional random and fixed resource allocation methods. The results demonstrate improved performance in terms of resource utilization, reduced unfulfilled demand, and fairness among VBS agents. The proposed combined resource allocation strategy effectively meets dynamic user requirements while maintaining system efficiency in C-RAN. Our work highlights the importance of integrating historical demand trends with real-time agent requests for improved long-term resource planning. © 2013 IEEE.

关键词： Radio access networks

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Optimizing Deep Learning for Brain Pathology Detection Using SqueezeNet with CT Imaging

Optimizing Deep Learning for Brain Pathology Detection Using...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Natarajan, Suresh Kumar Ghantasala, G S Pradeep Anthoniraj, S. Vidyullatha, Pellakuri Rajesh Sharma, R. Ananthanagu, U. School of Computer Science and Engineering Ramanagara India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram Guntur India

ISBN: (纸本)9798350385199

In this research, a explore the realm of medical imaging, specifically focusing on early brain pathology detection through the lens of deep learning. The primary objective is to optimize SqueezeNet, lightweight convolutional neural network architecture, for brain pathology detection using CT imaging. The motivation stems from the critical need for accurate and swift identification of brain abnormalities, with potential implications for patient outcomes. The meticulously preprocess a dataset of CT images of the human brain to enhance the model's ability to discern subtle pathology patterns. SqueezeNet, known for its efficiency, is employed, and optimization techniques during training enhance its performance. These findings suggest SqueezeNet's potential as a valuable tool in the medical field, offering a balance between efficiency and diagnostic accuracy. Throughout the discussion, A critically analyzes the implications of results, drawing comparisons with existing literature and addressing any encountered limitations. In this research contributes to the discourse on leveraging deep learning for brain pathology detection and the experimental results showcase promising outcomes, with a test accuracy of 78%. Precision, recall, and F1-score metrics indicate the model's proficiency in distinguishing between different pathologies: 87% for Hemorrhagic, 70% for Ischemic, and 79% for Normal cases. Confusion matrices and ROC curves provide comprehensive insights into the model's classification capabilities. The optimized SqueezeNet application demonstrates promise in enhancing diagnostic capabilities, with the obtained results providing valuable insights for future advancements in the intersection of deep learning and medical imaging, potentially impacting patient care and outcomes positively. © 2024 IEEE.

关键词： Medical imaging

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From One-Time to Two-Round Reusable Multi-signatures Without Nested Forking 22nd

From One-Time to Two-Round Reusable Multi-signatures Withou...

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22nd Theory of Cryptography Conference, TCC 2024

作者： Rotem, Lior Segev, Gil Yogev, Eylon Computer Science Department Stanford University Stanford United States School of Computer Science and Engineering Hebrew University of Jerusalem Jerusalem Israel Department of Computer Science Bar-Ilan University Ramat Gan Israel

ISBN: (纸本)9783031780196

Multi-signature schemes are gaining significant interest due to their blockchain applications. Of particular interest are two-round schemes in the plain public-key model that offer key aggregation, and whose security is based on the hardness of the DLOG problem. Unfortunately, despite substantial recent progress, the security proofs of the proposed schemes provide rather insufficient concrete guarantees (especially for 256-bit groups). This frustrating situation has so far been approached either by relying on the security of seemingly-stronger assumptions or by considering restricted classes of attackers (e.g., algebraic attackers, which are assumed to provide an algebraic justification of each group element that they produce). We present a complementing approach by constructing multi-signature schemes that satisfy two relaxed notions of security, whose applicability nevertheless ranges from serving as drop-in replacements to enabling expressive smart contract validation procedures. Our first notion, one-time unforgeability, extends the analogous single-signer notion by considering attackers that obtain a single signature for some message and set of signers of their choice. We construct a non-interactive one-time scheme based on any ring-homomorphic one-way function, admitting efficient instantiations based on the DLOG and RSA assumptions. Aggregated verification keys and signatures consist of two group elements and a single group element, respectively, and our security proof consists of a single application of the forking lemma (thus avoiding the substantial security loss exhibited by the proposed two-round schemes). Additionally, we demonstrate that our scheme naturally extends to a t-time scheme, where aggregated verification keys consist of t+1 group elements, while aggregated signatures still consist of a single group element. Our second notion, single-set unforgeability, considers attackers that obtain any polynomial number of signatures but are restricted to

关键词： Blockchain

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Analyze the impact of feature selection techniques in the early prediction of CKD

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International Journal of cognitive Computing in engineering 2024年第1期5卷 66-77页

作者： Hema, K. Meena, K. Pandian, Ramaraj Department of Computer Science and Engineering GITAM School of Technology GITAM University Bangalore India Netapp San JoseCA United States

Background: Chronic renal disease, often known as Chronic Kidney Disease (CKD), is an illness that causes a steady decline in kidney function. As per the World Health Organization survey, the incidence of CKD may increase from 10% to 13% by 2030. Because of the lack of symptoms in the initial phase, diagnosing CKD early on may be difficult. The key objective of this study is to develop a forecasting model for the early detection of chronic renal disease. Methods: In medical science, Machine Learning (ML) Techniques play a significant role in disease prediction despite numerous studies conducted to categorize CKD in patients using machine learning tools. Most researchers need to analyze the impact of feature selection techniques, yielding high-quality and reliable results. The efficiency of any Techniques/Algorithms depends on feature selection, feature extraction, and classifiers. In this work, the impact of feature selection is experimented with using the Exhaustive Feature Selection (EFS) method. For the early prediction of CKD, a comparative examination of machine learning classifiers, including Gradient Boost (GB), XGBoost, Decision Tree (DT), Random Forest (RF), and KNN (k-nearest neighbors), are utilized. Results: Two types of datasets, standard (New Model) & real-time data sets collected from the dialysis unit of a reputed hospital in Chennai, are used to carry out extensive experiment analysis. Various metrics, including Accuracy, Precision, Recall, and F1-score, are used to tabulate the results of experiments conducted to measure the performance of the proposed approach for various combinations of test and training data. Conclusion: CKD is an irreversible and silent disease;it might have a high impact on many people and begin to manifest themselves at an early age in life. This research paper analyses the effect of feature selection techniques on early CKD prediction. © 2023 The Authors

关键词： Forecasting

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Computing Challenges of UAV Networks: A Comprehensive Survey

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computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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