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

Summary

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


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

Published Papers
  • Smart Dynamic Traffic Monitoring and Enforcement System
  • Abstract Enforcement of traffic rules and regulations involves a wide range of complex tasks, many of which demand the use of modern technologies. variable speed limits (VSL) control is to change the current speed limit according to the current traffic situation based on the observed traffic conditions. The aim of this study is to provide a simulation-based methodological framework to evaluate (VSL) as an effective Intelligent Transportation System (ITS) enforcement system. The focus of the study is on measuring the effectiveness of the dynamic traffic control strategy on traffic performance and safety considering various performance indicators such as total travel time,… More
  •   Views:293       Downloads:262        Download PDF

  • M-IDM: A Multi-Classification Based Intrusion Detection Model in Healthcare IoT
  • Abstract In recent years, the application of a smart city in the healthcare sector via loT systems has continued to grow exponentially and various advanced network intrusions have emerged since these loT devices are being connected. Previous studies focused on security threat detection and blocking technologies that rely on testbed data obtained from a single medical IoT device or simulation using a well-known dataset, such as the NSL-KDD dataset. However, such approaches do not reflect the features that exist in real medical scenarios, leading to failure in potential threat detection. To address this problem, we proposed a novel intrusion classification architecture… More
  •   Views:376       Downloads:229        Download PDF

  • Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities
  • Abstract Recent advancements in hardware and communication technologies have enabled worldwide interconnection using the internet of things (IoT). The IoT is the backbone of smart city applications such as smart grids and green energy management. In smart cities, the IoT devices are used for linking power, price, energy, and demand information for smart homes and home energy management (HEM) in the smart grids. In complex smart grid-connected systems, power scheduling and secure dispatch of information are the main research challenge. These challenges can be resolved through various machine learning techniques and data analytics. In this paper, we have proposed a particle… More
  •   Views:487       Downloads:289        Download PDF

  • A Cyber Kill Chain Approach for Detecting Advanced Persistent Threats
  • Abstract The number of cybersecurity incidents is on the rise despite significant investment in security measures. The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks. This is primarily due to the sophistication of the attacks and the availability of powerful tools. Interconnected devices such as the Internet of Things (IoT) are also increasing attack exposures due to the increase in vulnerabilities. Over the last few years, we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks. Edge technology brings processing power closer to the… More
  •   Views:362       Downloads:458        Download PDF

  • 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
  •   Views:458       Downloads:273        Download PDF