Special Issue "AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication"

Submission Deadline: 31 May 2021
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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.


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.

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
  • Adversarial Attacks on Featureless Deep Learning Malicious URLs Detection
  • 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 accuracy and generalization to newly… More
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