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  • Open Access


    A Secure Device Management Scheme with Audio-Based Location Distinction in IoT

    Haifeng Lin1,2, Xiangfeng Liu2, Chen Chen2, Zhibo Liu2, Dexin Zhao3, Yiwen Zhang4, Weizhuang Li4, Mingsheng Cao5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 939-956, 2024, DOI:10.32604/cmes.2023.028656

    Abstract Identifying a device and detecting a change in its position is critical for secure devices management in the Internet of Things (IoT). In this paper, a device management system is proposed to track the devices by using audio-based location distinction techniques. In the proposed scheme, traditional cryptographic techniques, such as symmetric encryption algorithm, RSA-based signcryption scheme, and audio-based secure transmission, are utilized to provide authentication, non-repudiation, and confidentiality in the information interaction of the management system. Moreover, an audio-based location distinction method is designed to detect the position change of the devices. Specifically, the audio frequency response (AFR) of several… More >

  • Open Access


    CNN-Based RF Fingerprinting Method for Securing Passive Keyless Entry and Start System

    Hyeon Park1, SeoYeon Kim2, Seok Min Ko1, TaeGuen Kim2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1891-1909, 2023, DOI:10.32604/cmc.2023.039464

    Abstract The rapid growth of modern vehicles with advanced technologies requires strong security to ensure customer safety. One key system that needs protection is the passive key entry system (PKES). To prevent attacks aimed at defeating the PKES, we propose a novel radio frequency (RF) fingerprinting method. Our method extracts the cepstral coefficient feature as a fingerprint of a radio frequency signal. This feature is then analyzed using a convolutional neural network (CNN) for device identification. In evaluation, we conducted experiments to determine the effectiveness of different cepstral coefficient features and the convolutional neural network-based model. Our experimental results revealed that… More >

  • Open Access


    An Automatic Deep Neural Network Model for Fingerprint Classification

    Amira Tarek Mahmoud1,*, Wael A. Awad2, Gamal Behery2, Mohamed Abouhawwash3,4, Mehedi Masud5, Hanan Aljuaid6, Ahmed Ismail Ebada7

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2007-2023, 2023, DOI:10.32604/iasc.2023.031692

    Abstract The accuracy of fingerprint recognition model is extremely important due to its usage in forensic and security fields. Any fingerprint recognition system has particular network architecture whereas many other networks achieve higher accuracy. To solve this problem in a unified model, this paper proposes a model that can automatically specify itself. So, it is called an automatic deep neural network (ADNN). Our algorithm can specify the appropriate architecture of the neural network used and some significant parameters of this network. These parameters are the number of filters, epochs, and iterations. It guarantees the highest accuracy by updating itself until achieving… More >

  • Open Access


    Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

    Yun Fen Yong1,*, Chee Keong Tan1, Ian Kim Teck Tan2, Su Wei Tan1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1801-1818, 2023, DOI:10.32604/cmc.2023.032710

    Abstract A robust radio map is essential in implementing a fingerprint-based indoor positioning system (IPS). However, the offline site survey to manually construct the radio map is time-consuming and labour-intensive. Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys. This paper presents a novel fingerprint interpolator using a multi-path loss model (M-PLM) to create the virtual fingerprints from the collected sample data based on different signal paths from different access points (APs). Based on the historical signal data, the poor signal paths are identified using their standard deviations.… More >

  • Open Access


    Real and Altered Fingerprint Classification Based on Various Features and Classifiers

    Saif Saad Hameed, Ismail Taha Ahmed*, Omar Munthir Al Okashi

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 327-340, 2023, DOI:10.32604/cmc.2023.031622

    Abstract Biometric recognition refers to the identification of individuals through their unique behavioral features (e.g., fingerprint, face, and iris). We need distinguishing characteristics to identify people, such as fingerprints, which are world-renowned as the most reliable method to identify people. The recognition of fingerprints has become a standard procedure in forensics, and different techniques are available for this purpose. Most current techniques lack interest in image enhancement and rely on high-dimensional features to generate classification models. Therefore, we proposed an effective fingerprint classification method for classifying the fingerprint image as authentic or altered since criminals and hackers routinely change their fingerprints… More >

  • Open Access


    An Improved Hybrid Indoor Positioning Algorithm via QPSO and MLP Signal Weighting

    Edgar Scavino1,*, Mohd Amiruddin Abd Rahman1, Zahid Farid2

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 379-397, 2023, DOI:10.32604/cmc.2023.023824

    Abstract Accurate location or positioning of people and self-driven devices in large indoor environments has become an important necessity The application of increasingly automated self-operating moving transportation units, in large indoor spaces demands a precise knowledge of their positions. Technologies like WiFi and Bluetooth, despite their low-cost and availability, are sensitive to signal noise and fading effects. For these reasons, a hybrid approach, which uses two different signal sources, has proven to be more resilient and accurate for the positioning determination in indoor environments. Hence, this paper proposes an improved hybrid technique to implement a fingerprinting based indoor positioning, using Received… More >

  • Open Access


    Electrochemical Identification of Yulania spp. by Fingerprinting of Leaves Using Glassy Carbon Electrode

    Zhiguo Lu1, Yuhong Zheng1,*, Pengchong Zhang2, Boyuan Fan3, Aimin Yu4, Li Fu3,*

    Phyton-International Journal of Experimental Botany, Vol.91, No.11, pp. 2549-2558, 2022, DOI:10.32604/phyton.2022.021288

    Abstract In this communication, we used electrochemical sensor for recording the electrochemical profiles of eleven species of Yulania spp. from leaf extract. Two solvents and two buffer conditions were used for electrochemical fingerprints collection. Their electrochemical fingerprints can be converted to different patterns and consequently for species recognition. The results indicate the pattern recognition is much convenient than that of the recognition of species directly using voltammetric signal. The current information in electrochemical fingerprinting represents the type and amount of electrochemically active molecules, which linked to the genetic differences among the plants. Therefore, the electrochemical fingerprints were applied for further phylogenetic… More >

  • Open Access


    Loss of Fingerprints as a Side Effect of Capecitabine Therapy: Case Report and Literature Review

    Jian Zhao*1, Xia Zhang†1, Xiaonan Cui*, Di Wang*, Bin Zhang*‡, Liying Ban*

    Oncology Research, Vol.28, No.1, pp. 103-106, 2020, DOI:10.3727/096504019X15605078731913

    Abstract Hand–foot syndrome (HFS) is the main side effect of capecitabine and affects the compression zones of the body such as the palms and soles, causing numbness, paresthesias, skin swelling or erythema, scaling, chapping, hard nodule-like blisters, and severe pain. Loss of fingerprints is also observed in some cases. Severe cases of HFS are common in the review of clinical reports. However, loss of fingerprints has not received significant attention. Two reported cases of loss of fingerprints in The New England Journal of Medicine and The BMJ have drawn attention to this side effect of capecitabine. Loss of fingerprints has a… More >

  • Open Access


    A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

    Jie Chen1, Chengsheng Yuan1,2,*, Chen Cui2, Zhihua Xia1, Xingming Sun1,3, Thangarajah Akilan4

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 719-733, 2022, DOI:10.32604/cmc.2022.027984

    Abstract Fingerprint identification systems have been widely deployed in many occasions of our daily life. However, together with many advantages, they are still vulnerable to the presentation attack (PA) by some counterfeit fingerprints. To address challenges from PA, fingerprint liveness detection (FLD) technology has been proposed and gradually attracted people's attention. The vast majority of the FLD methods directly employ convolutional neural network (CNN), and rarely pay attention to the problem of over-parameterization and over-fitting of models, resulting in large calculation force of model deployment and poor model generalization. Aiming at filling this gap, this paper designs a lightweight multi-scale convolutional… More >

  • Open Access


    Fingerprint Agreement Using Enhanced Kerberos Authentication Protocol on M-Health

    A. S. Anakath1,*, S. Ambika2, S. Rajakumar3, R. Kannadasan4, K. S. Sendhil Kumar5

    Computer Systems Science and Engineering, Vol.43, No.2, pp. 833-847, 2022, DOI:10.32604/csse.2022.022329

    Abstract Cloud computing becomes an important application development platform for processing user data with high security. Service providers are accustomed to providing storage centers outside the trusted location preferred by the data owner. Thus, ensuring the security and confidentiality of the data while processing in the centralized network is very difficult. The secured key transmission between the sender and the receiver in the network is a huge challenge in managing most of the sensitive data transmission among the cloud network. Intruders are very active over the network like real authenticated user to hack the personal sensitive data, such as bank balance,… More >

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