EAI Endorsed Transactions on Smart Cities

Design a framework for IoT- Identification, Authentication and Anomaly detection using Deep Learning: A Review

Aimen Shoukat — 2023-01-17

The Internet of Things (IoT) connects billions of smart gadgets so that they may communicate with one another without the need for human intervention. With an expected 50 billion devices by the end of 2020, it is one of the fastest-growing industries in computer history. On the one hand, IoT technologies are critical in increasing a variety of real-world smart applications that can help people live better lives. The cross-cutting nature of IoT systems, on the other hand, has presented new security concerns due to the diverse components involved in their deployment. For IoT devices and their inherent weaknesses, security techniques such as encryption, authentication, permissions, network monitoring, \& application security are ineffective. To properly protect the IoT ecosystem, existing security solutions need to be strengthened. Machine learning and deep learning (ML/DL) have come a long way in recent years, and machine intelligence has gone from being a laboratory curiosity to being used in a variety of significant applications. The ability to intelligently monitor IoT devices is an important defense against new or negligible assaults. ML/DL are effective data exploration techniques for learning about 'normal' and 'bad' behavior in IoT devices and systems. Following a comprehensive literature analysis on Machine Learning methods as well as the importance of IoT security within the framework of different sorts of potential attacks, multiple DL algorithms have been evaluated in terms of detecting attacks as well as anomaly detection in this work. We propose a taxonomy of authorization and authentication systems in the Internet of Things based on the review, with a focus on DL-based schemes. The authentication security threats and problems for IoT are thoroughly examined using the taxonomy supplied. This article provides an overview of projects that involve the use of deep learning to efficiently and automatically provide IoT applications.

IoT-based Hybrid Wireless Network for Tourist Boat Tracking towards Smart Cities

Tuyen Truong — 2023-03-23

Moving and transporting by canoe and boat on rivers and canals is a cultural feature of the Mekong Delta and plays an important role in the economy and society. However, the management and use of equipment to support the monitoring of waterway transport vehicles in this area has yet to receive adequate investment and attention. Given the complicated evolution of the COVID-19 epidemic, it is critical to strengthen oversight of inland waterway management, as well as freight and passenger transportation. This paper presents the design and implementation of an IoT-based support system for managing and monitoring passenger ships and tourism activities in smart cities. This study proposes a hybrid wireless communication network solution that takes advantage of the strengths of LoRa and Zigbee wireless communication technologies, as well as telecommunication networks, to ensure that the system has a wide operating range of several kilometers, low power consumption, and can be deployed in areas where telecommunications are not available. Aside from tracking the journey and managing information about vehicles, drivers, and passengers, the system also aids in the collection of environmental parameters along river routes according to the travel route. An experimental evaluation of the system's operation was carried out for the tourist boat route between two famous tourist sites, Ninh Kieu Key and Cai Rang floating market in Can Tho city, Vietnam.

An Intelligent Machine Learning based Intrusion Detection System (IDS) for Smart cities networks

Muhammad Yaseen Ayub — 2023-03-23

INTRODUCTION: Internet of Things (IoT) along with Cloud based systems are opening a new domain of development. They have several applications from smart homes, Smart farming, Smart cities, smart grid etc. Due to IoT sensors operating in such close proximity to humans and critical infrastructure, there arises privacy and security issues. Securing an IoT network is very essential and is a hot research topic. Different types of Intrusion Detection Systems (IDS) have been developed to detect and prevent an unauthorized intrusion into the network.

OBJECTIVES: The paper presents a Machine Learning based light, fast and reliable Intrusion Detection System (IDS).

METHODS: Multiple Supervised machine learning algorithms are applied and their results are compared. Algorithms applied include Linear Discriminant analysis, Quadratic Discriminant Analysis, XG Boost, KNN and Decision Tree.

RESULTS: Simulation results showed that KNN Algorithm gives us the highest accuracy, followed by XG Boost and Decision Tree which are not far behind.

CONCLUSION: A fast, secure and intelligent IDS is developed using machine learning algorithms. The resulting IDS can be used in various types of networks especially in IoT based networks.

Investigation of Blockchain for COVID-19: A Systematic Review, Applications and Possible Challenges

Shah Hussain Badshah — 2023-03-23

Smart city is emerging application in which many Internet of Things (IoT) devices are embedded to perform overall monitoring and perform processing automatically. In smart city the authenticity is key problem and many users in the in smart city has faced challenges during COVID-19. The COVID-19 epidemic, a deadly virus, first appeared in the globe in 2019. The World Health Organization (WHO) states that it is almost certainly feasible to contain this virus in its early phases if some precautions are taken. To contain the infection, most nations declared emergencies both inside and outside their borders and prohibited travel. Artificial intelligence and blockchain are being used in smart city applications to monitor the general condition in the nation and reduce the mortality rate. Blockchain has also made it possible to safeguard patient medical histories and provide epidemic tracking. AI also offers the ideal, wanted answer for correctly identifying the signs. The primary goal of this study is to fully investigate blockchain technology and artificial intelligence (AI) in relation to COVID-19. A case study that was recently developed to identify and networked pathogens acquired important knowledge and data. Additionally, AI that can handle massive quantities of medical data and perform difficult jobs will be able to reduce the likelihood of intricacy in data analysis. Lastly, we highlight the present difficulties and suggest potential paths for addressing the 19 diseases in future circumstances.

An Efficient Substitution Box design with a chaotic logistic map and Linear Congruential Generator for secure communication in Smart cities

Muhammad Asim Hashmi — 2023-03-23

The study provides a unique method for creating an efficient substitution box (S-box) for advanced encryption standards using a Chaotic Logistic Map (CLM) and a Linear Congruential Generator (LCG) (AES) for secure communications in a smart city. The Pseudo-Random Number Generator (PRNG), which is further examined, is constructed using an extensive search of reasonable possibilities for the initial seed and set parameters. Using statistical testing, the performance analysis of the new S-box is assessed. Additionally, the resilience of differential, as well as linear cryptanalysis, is shown. It is derived using other features, including nonlinearity, the Bit Independence Criterion (BIC), and the Strict Avalanche Criterion (SAC). The suggested S-box has good potential and is usable for symmetric key cryptography, according to the features of the new S-cryptographic box.

Editorial: Welcome to the first issue of Volume 7 of the EAI Endorsed Transactions on Smart Cities

Mohammad Derawi — 2023-03-30