Special Issue "Security and Privacy issues for various Emerging Technologies and Future Trends"

Submission Deadline: 30 September 2021 (closed)
Guest Editors
Dr. Muhammad Ahmad, National University of Computer and Emerging Sciences, Pakistan.
Dr. Muhammad Asif, National Textile University, Faisalabad, Pakistan.
Dr. Muhammad Umar Aftab, National University of Computer and Emerging Sciences, Pakistan.


With the continuous development of emerging technologies such as secure organizational systems, social networks, e-commerce, and 5G systems, the collecting, processing, and analysis of the various types of organizational data has become very convenient and common. This often allows confidential information more susceptible to misuse, so it is increasingly important to explore safe processes and optimized solutions for evolving technologies.

Security System is an important concept that secures the data from inside and outside security threats. Usually, a massive part of the threats comes internally from the organization. However, access control is a way to control the unauthorized access of users because it grants or revokes access to authorized users. There are numerous ways to implement access control by using various models. There are many techniques to make the information secure but there is a need to provide a security solution that must make the information secure in an efficient way.

Nowadays, practitioners are working to provide security solutions by reducing the complexity such as increasing the number of attributes, which can provide tight security. On contrary, more number of attributes increases the complexity of the model. In this way, security experts have to implement tight security by using fewer attributes. Therefore, there is a need to reduce computational overhead in complex security solutions. The security model should be complex in terms of security implementation and hard to compromise but it should be efficient in terms of memory, computational cost, and scalability.

Thus, this special issue aims to present a collection of new and innovative Artificial Intelligence trends and techniques so that secure solutions can be provided for emerging technologies and complex networks. Original research, as well as the review articles, are welcome. 

• Security trends in Machine Learning
• Security Attacks and Threat analysis by using AI
• Efficient Data Encryption Schemes in AI
• Lightweight access control schemes in IoT
• Hybrid Security Solutions for IoT and Blockchain
• Access Control and Authorization
• Complex and Secure Networks

Published Papers
  • Digital Watermarking Scheme for Securing Textual Database Using Histogram Shifting Model
  • Abstract Information security is one of the most important methods of protecting the confidentiality and privacy of internet users. The greater the volume of data, the more the need to increase the security methods for protecting data from intruders. This task can be challenging for researchers in terms of managing enormous data and maintaining their safety and effectiveness. Protection of digital content is a major issue in maintaining the privacy and secrecy of data. Toward this end, digital watermarking is based on the concept of information security through the insertion and detection of an embedded watermark in an efficient manner. Recent… More
  •   Views:516       Downloads:415        Download PDF

  • Robust Authentication and Session Key Agreement Protocol for Satellite Communications
  • Abstract Satellite networks are recognized as the most essential communication infrastructures in the world today, which complement land networks and provide valuable services for their users. Extensive coverage and service stability of these networks have increased their popularity. Since eavesdropping and active intrusion in satellite communications are much easier than in terrestrial networks, securing satellite communications is vital. So far, several protocols have been proposed for authentication and key exchange of satellite communications, but none of them fully meet the security requirements. In this paper, we examine one of these protocols and identify its security vulnerabilities. Moreover, we propose a robust… More
  •   Views:562       Downloads:403        Download PDF

  • A Multi-Factor Authentication-Based Framework for Identity Management in Cloud Applications
  • Abstract User's data is considered as a vital asset of several organizations. Migrating data to the cloud computing is not an easy decision for any organization due to the privacy and security concerns. Service providers must ensure that both data and applications that will be stored on the cloud should be protected in a secure environment. The data stored on the public cloud will be vulnerable to outside and inside attacks. This paper provides interactive multi-layer authentication frameworks for securing user identities on the cloud. Different access control policies are applied for verifying users on the cloud. A security mechanism is… More
  •   Views:682       Downloads:535        Download PDF

  • Automated Patient Discomfort Detection Using Deep Learning
  • Abstract The Internet of Things (IoT) has been transformed almost all fields of life, but its impact on the healthcare sector has been notable. Various IoT-based sensors are used in the healthcare sector and offer quality and safe care to patients. This work presents a deep learning-based automated patient discomfort detection system in which patients’ discomfort is non-invasively detected. To do this, the overhead view patients’ data set has been recorded. For testing and evaluation purposes, we investigate the power of deep learning by choosing a Convolution Neural Network (CNN) based model. The model uses confidence maps and detects 18 different… More
  •   Views:590       Downloads:548        Download PDF

  • Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold
  • Abstract Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type, scenarios and level of blurriness. In this paper, we propose an effective method for blur detection and segmentation based on transfer learning concept. The proposed method consists of two separate steps. In the first step, genetic programming (GP) model is developed that quantify the amount of blur for each pixel in the image. The GP model method uses the multi-resolution features of the image and it provides an improved blur map. In the second phase,… More
  •   Views:619       Downloads:514        Download PDF

  • Convolutional Neural Network for Histopathological Osteosarcoma Image Classification
  • Abstract Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate. Early diagnosis may increase the chances of treatment and survival however the process is time-consuming (reliability and complexity involved to extract the hand-crafted features) and largely depends on pathologists’ experience. Convolutional Neural Network (CNN—an end-to-end model) is known to be an alternative to overcome the aforesaid problems. Therefore, this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet (a high-class imbalanced dataset). Though, during training, class-imbalanced data can negatively affect the performance of… More
  •   Views:746       Downloads:583        Download PDF