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Enhanced Security with Improved Defensive Routing Mechanism in Wireless Sensor Networks

computer Systems science & engineering 2023年第6期45卷 2795-2810页

作者： R.Sabitha C.Gokul Prasad S.Karthik Department of Computer Science&Engineering SNS College of TechnologyCoimbatoreTamilnaduIndia Department of Electronics and Communication Engineering SNS College of EngineeringCoimbatoreTamilnaduIndia

In recent scenario of Wireless Sensor Networks(WSNs),there are many application developed for handling sensitive and private data such as military information,surveillance data,tracking,***,the sensor nodes of WSNs are distributed in an intimidating region,which is non-rigid to *** recent research domains of WSN deal with models to handle the WSN communications against malicious attacks and *** traditional models,the solution has been made for defending the networks,only to specific ***,in real-time applications,the kind of attack that is launched by the adversary is not ***,on developing a security mechanism for WSN,the resource constraints of sensor nodes are also to be *** that note,this paper presents an Enhanced Security Model with Improved Defensive Routing Mechanism(IDRM)for defending the sensor network from various ***,for efficient model design,the work includes the part of feature evaluation of some general attacks of *** IDRM also includes determination of optimal secure paths and Node security for secure routing *** performance of the proposed model is evaluated with respect to several factors;it is found that the model has achieved better security levels and is efficient than other existing models in WSN *** is proven that the proposed IDRM produces 74%of PDR in average and a minimized packet drop of 38%when comparing with the existing works.

关键词： Enhanced security model wireless sensor networks improved defensive routing mechanism secure paths node security

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FSACE: finite state automata-based client-side encryption for secure data deduplication in cloud computing

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International Journal of Cloud Computing 2023年第6期12卷 531-555页

作者： Kodada, Basappa B. Mello, Demian Antony D. Canara Engineering College VTU Belagavi Mangalore India Department of Computer Science and Engineering Canara Engineering College VTU Belagavi KA Mangalore India

Digital data is growing vastly and is generated from different sources in a structured manner, and the management of this high volume of data is critical, forcing clients to use storage services. The cloud storage server (CSS) has accumulated massive duplicate data. In reality, the computation overhead is increased on the storage server to manage these data with security aspects. Data deduplication is a widely used technique to reduce overhead on CSS. Several approaches have been proposed to address the issues of secure data deduplication (SDD). The convergent encryption (CE) is widely used for SDD to reduce computational overhead and improve storage efficiency, but CE algorithm faces threats from inner and outer adversaries. This paper proposes finite-state automata-based client-side encryption (FSACE) protocol to accomplish SDD to provide confidentiality, integrity and access control on data. The security analysis indicates, FSACE is secure enough to protect data from adversaries. We also evaluate the performance of protocol through experimentation and results that show decrease in computation overhead. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Cryptography

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Abnormal Crowd Behavior Detection Using Optimized Pyramidal Lucas-Kanade Technique

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Intelligent Automation & Soft Computing 2023年第2期35卷 2399-2412页

作者： G.Rajasekaran J.Raja Sekar Department of information technology mepco schlenk engineering collegesivakasi626005india Department of computer science and engineering mepco schlenk engineering collegesivakasi626005india

Abnormal behavior detection is challenging and one of the growing research areas in computer *** main aim of this research work is to focus on panic and escape behavior detections that occur during unexpected/uncertain *** this work,Pyramidal Lucas Kanade algorithm is optimized using EME-HOs to achieve the *** stage,OPLKT-EMEHOs algorithm is used to generate the opticalﬂow from *** stage,the MIIs opticalﬂow is applied as input to 3 layer CNN for detect the abnormal crowd *** of Minnesota(UMN)dataset is used to evaluate the proposed *** experi-mental result shows that the proposed method provides better classiﬁcation accu-racy by comparing with the existing *** method provides 95.78%of precision,90.67%of recall,93.09%of f-measure and accuracy with 91.67%.

关键词： Crowd behavior analysis anomaly detection Motion Information

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Blockchain architecture in upcoming digital world 4th

Blockchain architecture in upcoming digital world

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4th Annual International Conference on Data science, Machine Learning and Blockchain Technology, AICDMB 2023

作者： Promod Kumar, B.M. Manoj Gowda, K.S. Heemashree, S.S. Siddesh Kumar, N.M. Tejas, S. Haran, Pranadhi Vikas, C.M. Department of Computer Science and Engineering PES College of Engineering Mandya India Department of Information Science and Engineering PES College of Engineering Mandya India Department of Mechanical Engineering PES College of Engineering Mandya India

ISBN: (纸本)9781032426853

Blockchain architecture is a multi-layered system with various components that work together to ensure stability, security, and efficiency. The bottom layer, the Data Layer, holds transactions and data, while the Consensus Layer ensures accuracy and validates transactions. The Network Layer manages communication and data transmission, and the Application Layer provides user access. The Contract Layer executes smart contracts and defines rules, and the Security Layer protects the system from attacks. Blockchain architecture is widely used in various industries including finance, healthcare, supply chain, and voting systems, providing secure, transparent, and immutable data. The combination of blockchain and AI technology results in secure, decentralized and automated execution of agreements, transforming industries such as finance, supply chain and healthcare. © 2024 The Author(s).

关键词： Blockchain

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Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第3期6卷 710-724页

作者： Dalvi, Prachi Kalbande, Dhananjay R. Rathod, Surendra Singh Dalvi, Harshal Agarwal, Amey Bharatiya Vidya Bhavans Sardar Patel Institute of Technology Department of Computer Engineering Mumbai400058 India Fr. Conceicao Rodrigues College of Engineering Department of Electronics and Computer Science Mumbai400050 India Dwarkadas J Sanghvi College of Engineering Department of Information Technology Mumbai400056 India

Skin health is a critical concern for humans, especially in geographical areas where environmental conditions and lifestyle factors adversely affect their condition, leading to a prevalence of skin diseases. This issue is exacerbated in rural regions, like parts of India, where a notable dermatologist shortage exists, leading to overlooked skin diseases. In response, the use of medicinal plants for dermatological purposes has been a longstanding tradition. However, traditional plant identification often relies on a single attribute, such as leaves or flowers, which can be unreliable due to seasonal variations. This article introduces a novel approach for accurately identifying medicinal plants using a multiattribute deep convolutional neural network. This approach aims to bridge the gap in healthcare access by empowering individuals to recognize and utilize these plants effectively. Our objective is to develop a robust deep CNN model trained on a diverse dataset of images encompassing leaves, trunks, and seeds of medicinal plants associated with skin health. Our findings demonstrate that the model achieves high accuracy in plant identification, effectively addressing the limitations of single-attribute methods. This research not only contributes to the field of medicinal plant classification but also empowers individuals to make informed decisions about their skin health while preserving valuable traditional knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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The Challenges and Opportunities of Smart Cities Infrastructure

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SN computer science 2024年第6期5卷 713页

作者： Abuali, Amer Alharthi, Wejdan Althbyani, Mariam Ghabban, Fahad Ameerbakhsh, Omair Alfadli, Ibrahim Al-Shehri, Ashwaq Hasen Fallatah, Najmah Adel College of Computer Science and Engineering Information System Department Taibah University

Information communications technology (ICT) refers to the technology used for communication and managing information must be utilized to enhance the environment, economics, mobility, and governance, among other facets of urban life, in order to create smart cities. However this progress is frequently accompanied by a number of challenges and unfavorable outcomes. This study aims to uncover ICT difficulties related to smart city infrastructure through an extensive literature review. In addition, a survey was carried out among Saudi nationals to find out what they thought about, anticipated, and worried about in terms of smart city concepts and features. The study also looked at a literature checklist of ICT problems, which included dangers to information security, incompatibilities across systems, privacy concerns, and deficiencies in digital capabilities. Based on the findings it appears that privacy breaches and information security concerns are the most important issues. This is explained by heightened susceptibility and potential, cyberattacks and a pervasive ignorance of the protection of personal data. Because of the anticipated high costs and difficulties with adaption and utilization, the public is concerned about incompatibility between systems and services. Furthermore, older individuals and those with lesser educational achievement have different digital skills. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Compatibility ICT challenges Information insecurity Privacy Smart city Technology skills

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Unmasking Hypertension Through a Fusion of Human Lifestyle Data using Machine Learning Algorithms 6

Unmasking Hypertension Through a Fusion of Human Lifestyle D...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Vishnu Priya, V. Nisanth, G. Nivethaa, R.P. Vijay, Srinidhi Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai India Department of Computer Applications Kongu Engineering College Erode Perundurai India Department of Computer Technology UG Kongu Engineering College Erode Perundurai India

ISBN: (纸本)9798331522667

Hypertension, also referred to as high blood pressure, is a condition arising from the consistently high blood pressure against artery walls. The volume and output of blood from the heart primarily control blood pressure under stiff or tight arterial conditions. High pulse pressure leads to increased blood pressure, since the pumping of more blood makes the arteries inactive. The World Health Organization has a comprehensive guideline in which an evidence-based pharmacological approach has been recommended for managing adult patients with hypertension, not neglecting to mention lifestyle modification in order to support medicinal treatment. Machine learning may revolutionize the way hypertension treatment is projected for the management of personalized treatment routines for patients based on their characteristics. ML provides great promise in seeking ideal medication and predicting responses thereof against massive sets of data for hypertensive patients. The techniques allow for the determination of the individual risk for hypertension and ensure that treatment protocols are optimal. Compared to methods used to predict hypertension in the past, ML algorithms show a marked improvement in performance. © 2025 IEEE.

关键词： Personalized medicine

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Determination of the time location points and investigation of the body sensor network using RATDMA and PATDMA

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9043-9055页

作者： Mohammed Mustafa, M. Kalpana Devi, S. Althaf Ali, A. Gunavathie, M.A. Department of Information Technology Sri Krishna College of Engineering & Technology Coimbatore India Department of Computer Science and Engineering RV University Bengaluru India Andhra Pradesh India Department of Information Technology Panimalar Engineering College Chennai India

Wireless body sensor networks have gained significant importance across diverse fields, including environmental monitoring, healthcare, and sports. This research is concentrated on sports applications, specifically exploring the viability of a wireless body area network tailored for high-performing athletes. The paper is divided into three sections. First, the design of the node location that is used for real-time monitoring of a sportsperson in which the node position, such as the human thigh, foot, arm, wrist, and chest, was estimated and the best position was selected. Second, the accuracy of an application when related to the other schemes such as TDMA with ZigBee and RA-TDMA & PA-TDMA was done. The reliability using RA-TDMA performed well and showed approximately 98% reliability. Finally, the features of wireless communiqués that affect the presentation of the network for RA-TDMA were estimated, such as delay and jitter. These findings collectively contribute to advancing the understanding of optimizing wireless body sensor networks for sports applications, with notable achievements including the identification of the arm as the optimal sensor placement, achieving a 98% success rate, and surpassing alternative techniques in network performance parameters like packet delivery rate. © 2024 – IOS Press.

关键词： Location

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Toward Optimal Periodic Crowd Tracking via Unmanned Aerial Vehicle

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computer Modeling in engineering & sciences 2023年第10期137卷 233-263页

作者： Khalil Chebil Skander Htiouech Mahdi Khemakhem Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharj11942Saudi Arabia Data Engineering and Semantics Research Unit Faculty of Sciences of SfaxUniversity of SfaxSfaxTunisia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Mathematics and Business Intelligence College of Electronics and Telecommunications Engineering of SfaxUniversity of SfaxSfaxTunisia

Crowd management and analysis(CMA)systems have gained a lot of interest in the vulgarization of unmanned aerial vehicles(UAVs)*** tracking using UAVs is among the most important services provided by a *** this paper,we studied the periodic crowd-tracking(PCT)*** consists in usingUAVs to follow-up crowds,during the life-cycle of an open crowded area(OCA).Two criteria were considered for this *** first is related to the CMA initial investment,while the second is to guarantee the quality of service(QoS).The existing works focus on very specified assumptions that are highly committed to CMAs applications *** study outlined a new binary linear programming(BLP)model to optimally solve the PCT motivated by a real-world application study taking into consideration the high level of *** closely approach different real-world contexts,we carefully defined and investigated a set of parameters related to the OCA characteristics,behaviors,and theCMAinitial infrastructure investment(e.g.,UAVs,charging stations(CSs)).In order to periodically update theUAVs/crowds andUAVs/CSs assignments,the proposed BLP was integrated into a linear algorithm called PCTs *** main objective was to study the PCT problem fromboth theoretical and numerical *** prove the PCTs solver effectiveness,we generated a diversified set of PCTs instances with different scenarios for simulation *** empirical results analysis enabled us to validate the BLPmodel and the PCTs solver,and to point out a set of new challenges for future research directions.

关键词： Unmanned aerial vehicles periodic crowd-tracking problem open crowded area optimization binary linear programming crowd management and analysis system

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Pattern Analysis and Regressive Linear Measure for Botnet Detection

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computer Systems science & engineering 2022年第10期43卷 119-139页

作者： B.Padmavathi B.Muthukumar Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyChennaiIndia Department of Computer Science and Engineering Easwari Engineering CollegeChennaiIndia Department of Computer Science and Engineering United Institute of TechnologyCoimbatoreIndia

Capturing the distributed platform with remotely controlled compromised machines using botnet is extensively analyzed by various ***,certain limitations need to be addressed *** provisioning of detection mechanism with learning approaches provides a better solution more broadly by saluting multi-objective *** bots’patterns or features over the network have to be analyzed in both linear and non-linear *** linear and non-linear features are composed of high-level and low-level *** collected features are maintained over the Bag of Features(BoF)where the most influencing features are collected and provided into the classifier ***,the linearity and non-linearity of the threat are evaluated with Support Vector Machine(SVM).Next,with the collected BoF,the redundant features are eliminated as it triggers overhead towards the predictor ***,a novel Incoming data Redundancy Elimination-based learning model(RedE-L)is built to classify the network features to provide robustness towards BotNets *** simulation is carried out in MATLAB environment,and the evaluation of proposed RedE-L model is performed with various online accessible network traffic dataset(benchmark dataset).The proposed model intends to show better tradeoff compared to the existing approaches like conventional SVM,C4.5,RepTree and so ***,various metrics like Accuracy,detection rate,Mathews Correlation Coefficient(MCC),and some other statistical analysis are performed to show the proposed RedE-L model's *** F1-measure is 99.98%,precision is 99.93%,Accuracy is 99.84%,TPR is 99.92%,TNR is 99.94%,FNR is 0.06 and FPR is 0.06 respectively.

关键词： BotNet threat intrusion features linearity and non-linearity redundancy regressive linear measure classification redundancy eliminationbased learning model

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