Special Issue "Machine Learning-based Secured and Privacy-preserved Smart City"

Submission Deadline: 18 October 2020 (closed)
Guest Editors
Prof. Fazlullah Khan, RoZetta Institute (formerly CMCRC), Australia
Prof. Mian Ahmad Jan, Northwetern Polytechnical University, China
Prof. Alireza Jolfaei, Macquarie University, Australia
Prof. Lie-Liang Yang, Southampton University, UK
Prof. Ateeq ur Rehman, Abdul Wali Khan University Mardan, Pakistan


The developments in communication technologies have given rise to the Internet of Things (IoT). The IoT growth is very fast due to connecting of smart devices and objects to the Internet. As a result, it is considered as the backbone of smart city. By the end of 2020, it is expected that there will be 37 billion things connected with the Internet in the context of smart city. These devices collect data related to smart healthcare, green energy, and public safety that provides useful information to a variety of smart city's applications. For example, in a smart city application, intelligence of IoT will enable the things to perform human-like thinking and reasoning. In this context, machine learning (ML) and deep learning (DL) are considered as key enabling technologies for making things smarter by providing information inferences and intelligence to them. In a smart city scenario, IoT connects everything in world that puts user's privacy at stack and opens windows to numerous security issues. It is because, anyone can access certain IoT devices from anywhere without the user permission. The IoT and ML/DL in a smart city are the key technologies that deeply effect our lives. The ML/DL techniques used in the IoT assists quick and efficient data mining that can provide important and useful decisions for smart city applications. However, ML/DL techniques in the IoT face several security and privacy issues. For example, the training models are sensitive to a tiny perturbations in the input data provided by a malicious user. This results in various attacks that mislead the training models and also acquire the user's information. Therefore, in this special issue, we aim to focus on ML/DL techniques for security and privacy in the IoT-based smart city. This special issue will cover novel research approaches in ML/DL techniques in security and privacy in the IoT-based smart city scenarios.


Topics of interest include but are not limited to the following:

• Privacy and Security Issues in the IoT-enabled smart city

• Privacy and Security Issues in AI-based IoT-enabled smart city

• Privacy and Security Issues in ML/DL-enabled smart city

• Novel Theories, Concepts, and Architectures in ML/DL-based IoT-enabled smart city

• ML/DL-enabled Attacks and Defense mechanisms in Hardware Level IoT Systems

• ML/DL-enabled Attack and Defense mechanisms in Cloud Computing Systems for smart city

• Privacy and Security Issues in ML/DL for IoT deployment and operation in smart city

• Secured and Privacy-preserved AI and IoT assisted smart city applications

• ML/DL-enabled real-time IoT data analytics in smart city

• AI-enabled sensing and decision-making for IoT-enabled smart city

• ML/DL-enabled cloud/edge computing systems for IoT-enabled smart city

Security, Privacy, Machine Learning, Deep Learning, Artificial Intelligence, Security Attacks, Smart City, Internet of Things, Blockchain, Trust. Defense Mechanisms.

Published Papers
  • Security Requirement Management for Cloud-Assisted and Internet of Things—Enabled Smart City
  • Abstract The world is rapidly changing with the advance of information technology. The expansion of the Internet of Things (IoT) is a huge step in the development of the smart city. The IoT consists of connected devices that transfer information. The IoT architecture permits on-demand services to a public pool of resources. Cloud computing plays a vital role in developing IoT-enabled smart applications. The integration of cloud computing enhances the offering of distributed resources in the smart city. Improper management of security requirements of cloud-assisted IoT systems can bring about risks to availability, security, performance, confidentiality, and privacy. The key reason… More
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