Special Issues
Table of Content

AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication

Submission Deadline: 31 May 2021 (closed) View: 28

Guest Editors

Dr. Muhammad Ahmad, National University of Computer & Emerging Sciences, Pakistan.
Prof. Dr. Ali Kashif Bashir, Manchester Metropolitan University, United Kingdom.
Prof. Dr. Diego Alberto Oliva Navarro, Universidad de Guadalajara, Mexico.

Summary

Smartphone and Smartwatch users exponentially increased by 3 billion and are expected to further grow by several hundred million in near future. Boosted by information and communication technologies, smartphones and Smartwatches are becoming a more and more powerful and thus trustworthy inseparable companion of our lives. Moreover, Smartphones and Smartwatches have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification and authentication processes in a controlled environment (laboratory process). Therefore, when a Smartphone or Smartwatch is stolen, a thief can have access to the owner's personal information and services against the stored passwords that have forced the community to study the security implications of these devices. As a result of this potential scenario, this Special Collection aims to collect new automatic legitimate user identification systems and possible innovative/technical reviews for future research directions.


Keywords

Smartphone and Smartwatch based Physical Activity Recognition
Legitimate User Identification / Authentication
Information and Communication Technologies
Multi-level and Multi-sensor data fusion
IoT and security

Published Papers


  • Open Access

    ARTICLE

    Adaptive Runtime Monitoring of Service Level Agreement Violations in Cloud Computing

    Sami Ullah Khan, Babar Nazir, Muhammad Hanif, Akhtar Ali, Sardar Alam, Usman Habib
    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4199-4220, 2022, DOI:10.32604/cmc.2022.020852
    (This article belongs to the Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract The cloud service level agreement (SLA) manage the relationship between service providers and consumers in cloud computing. SLA is an integral and critical part of modern era IT vendors and communication contracts. Due to low cost and flexibility more and more consumers delegate their tasks to cloud providers, the SLA emerges as a key aspect between the consumers and providers. Continuous monitoring of Quality of Service (QoS) attributes is required to implement SLAs because of the complex nature of cloud communication. Many other factors, such as user reliability, satisfaction, and penalty on violations are also… More >

  • Open Access

    ARTICLE

    Towards Securing Machine Learning Models Against Membership Inference Attacks

    Sana Ben Hamida, Hichem Mrabet, Sana Belguith, Adeeb Alhomoud, Abderrazak Jemai
    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4897-4919, 2022, DOI:10.32604/cmc.2022.019709
    (This article belongs to the Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract From fraud detection to speech recognition, including price prediction, Machine Learning (ML) applications are manifold and can significantly improve different areas. Nevertheless, machine learning models are vulnerable and are exposed to different security and privacy attacks. Hence, these issues should be addressed while using ML models to preserve the security and privacy of the data used. There is a need to secure ML models, especially in the training phase to preserve the privacy of the training datasets and to minimise the information leakage. In this paper, we present an overview of ML threats and vulnerabilities,… More >

  • Open Access

    ARTICLE

    A Machine Learning Approach for Early COVID-19 Symptoms Identification

    Omer Ali, Mohamad Khairi Ishak, Muhammad Kamran Liaquat Bhatti
    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3803-3820, 2022, DOI:10.32604/cmc.2022.019797
    (This article belongs to the Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract Symptom identification and early detection are the first steps towards a health condition diagnosis. The COVID-19 virus causes pneumonia-like symptoms such as fever, cough, and shortness of breath. Many COVID-19 contraction tests necessitate extensive clinical protocols in medical settings. Clinical studies help with the accurate analysis of COVID-19, where the virus has already spread to the lungs in most patients. The majority of existing supervised machine learning-based disease detection techniques are based on clinical data like x-rays and computerized tomography. This is heavily reliant on a larger clinical study and does not emphasize early symptom… More >

  • Open Access

    ARTICLE

    A Compromise Programming to Task Assignment Problem in Software Development Project

    Ngo Tung Son, Jafreezal Jaafar, Izzatdin Abdul Aziz, Bui Ngoc Anh, Hoang Duc Binh, Muhammad Umar Aftab
    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3429-3444, 2021, DOI:10.32604/cmc.2021.017710
    (This article belongs to the Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract The scheduling process that aims to assign tasks to members is a difficult job in project management. It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process. This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically. The generated schedule directs the project to be completed with the shortest critical path, at the minimum cost, while maintaining its quality. There are several real-world business constraints related to human resources, the similarity of the tasks added to the optimization model, and the literature’s traditional… More >

  • Open Access

    ARTICLE

    Evolution-Based Performance Prediction of Star Cricketers

    Haseeb Ahmad, Shahbaz Ahmad, Muhammad Asif, Mobashar Rehman, Abdullah Alharbi, Zahid Ullah
    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1215-1232, 2021, DOI:10.32604/cmc.2021.016659
    (This article belongs to the Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract Cricket databases contain rich and useful information to examine and forecasting patterns and trends. This paper predicts Star Cricketers (SCs) from batting and bowling domains by employing supervised machine learning models. With this aim, each player’s performance evolution is retrieved by using effective features that incorporate the standard performance measures of each player and their peers. Prediction is performed by applying Bayesian-rule, function and decision-tree-based models. Experimental evaluations are performed to validate the applicability of the proposed approach. In particular, the impact of the individual features on the prediction of SCs are analyzed. Moreover, the More >

  • Open Access

    ARTICLE

    Adversarial Attacks on Featureless Deep Learning Malicious URLs Detection

    Bader Rasheed, Adil Khan, S. M. Ahsan Kazmi, Rasheed Hussain, Md. Jalil Piran, Doug Young Suh
    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 921-939, 2021, DOI:10.32604/cmc.2021.015452
    (This article belongs to the Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)
    Abstract Detecting malicious Uniform Resource Locators (URLs) is crucially important to prevent attackers from committing cybercrimes. Recent researches have investigated the role of machine learning (ML) models to detect malicious URLs. By using ML algorithms, first, the features of URLs are extracted, and then different ML models are trained. The limitation of this approach is that it requires manual feature engineering and it does not consider the sequential patterns in the URL. Therefore, deep learning (DL) models are used to solve these issues since they are able to perform featureless detection. Furthermore, DL models give better… More >

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