Applications of Artificial Intelligence and Machine learning in smart cities

In recent years, the wide adoption of the modern Internet of Things (IoT) paradigm has led to the invention of smart cities. Smart cities operate in real-world time to promote ease and quality of life in urban cities. The network traffic of a smart city via IoT systems is growing exponentially and introducing new cybersecurity challenges since these IoT devices are being connected to sensors that are directly connected to massive cloud servers. In order to mitigate these cyberattacks, the developers need to enhance new techniques for detecting infected IoT devices. In this paper, to address the IoT cybersecurity threats in a smart city, we propose an Anomaly Detection-IoT (AD-IoT) system, which is an intelligent anomaly detection based on Random Forest machine learning algorithm. The proposed solution can effectively detect compromised IoT devices at distributed fog nodes. To evaluate our model, we utilized modern dataset to illustrate the model’s accuracy. Our findings show that the AD-IoT can effectively achieve highest classification accuracy of 99.34% with lowest false positive rate.

AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning

An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks

A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework

Anomaly detection has been used for decades to identify and extract anomalous components from data. Many techniques have been used to detect anomalies. One of the increasingly significant techniques is Machine Learning (ML), which plays an important role in this area. In this research paper, we conduct a Systematic Literature Review (SLR) which analyzes ML models that detect anomalies in their application. Our review analyzes the models from four perspectives; the applications of anomaly detection, ML techniques, performance metrics for ML models, and the classification of anomaly detection. In our review, we have identified 290 research articles, written from 2000-2020, that discuss ML techniques for anomaly detection. After analyzing the selected research articles, we present 43 different applications of anomaly detection found in the selected research articles. Moreover, we identify 29 distinct ML models used in the identification of anomalies. Finally, we present 22 different datasets that are applied in experiments on anomaly detection, as well as many other general datasets. In addition, we observe that unsupervised anomaly detection has been adopted by researchers more than other classification anomaly detection systems. Detection of anomalies using ML models is a promising area of research, and there are a lot of ML models that have been implemented by researchers. Therefore, we provide researchers with recommendations and guidelines based on this review.

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Machine Learning for Anomaly Detection: A Systematic Review

The number of zero-day attacks has been exponentially increasing due to a variety of heterogeneous Internet of Things (IoT) protocols. Many centralized-based techniques have been introduced to identify malicious activities in IoT environments. However, these techniques have suffered including a high latency to satisfy IoT requirements. This paper proposes a fog-based attack detection (FBAD) framework using an ensemble of online sequential extreme learning machine (EOS-ELM) for efficiently detecting malicious activities. We indicate a high-level view of the efficient proposed framework for deploying the distributed attack detection technique in fog computing due to high accuracy and low latency, as it is closer to the IoT devices at the network edge. Furthermore, we compare the performance of our framework with other existing approaches including ELM and OS-ELM. The results of the experiment demonstrate that distributed architecture outperforms centralized architecture in terms of the detection time and classification accuracy.

FBAD: Fog-based Attack Detection for IoT Healthcare in Smart Cities

Nowadays, technology has been evolving rapidly. Due to the consequent impact of smart technologies, it becomes a ubiquitous part of life. These technologies have led to the emergence of smart cities that are geographic areas driven by advanced information and communication technologies. In the context of smart cities, IoT, blockchain, and fog computing have been found as the significant drivers of smart initiates. In this recognition, the present study is focused on delineating the impact and potential of blockchain, IoT, and fog computing on healthcare services in the context of smart cities. In pursuit of this objective, the study has conducted a systematic review of literature that is most relevant to the topic of the paper. In order to select the most relevant and credible articles, the researcher has used PRISMA and AMSTAR that have culminated in the 10 most relevant articles for the present study. The findings revealed that IoT, blockchain, and fog computing had become drivers of efficiency in the healthcare services in smart cities. Among the three technologies, IoT has been found to be widely incorporated. However, it is found to be lacking in terms of cost efficiency, data privacy, and interoperability of data. In this recognition, blockchain technology and fog computing have been found to be more relevant to the healthcare sector in smart cities. Blockchain has been presented as a promising technology for ensuring the protection of private data, creating a decentralized database, and improving the interoperability of data while fog computing has been presented as the promising technology for low-cost remote monitoring, reducing latency and increasing efficiency.

/pdf/blockchain-and-fog-computing-in-iot-driven-healthcare-2j8jwtp9.pdf

Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities

Revolution in healthcare can be experienced with the advancement of smart sensorial things, Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Internet of Medical Things (IoMT), and edge analytics with the integration of cloud computing. Connected healthcare is receiving extraordinary contemplation from the industry, government, and the healthcare communities. In this study, several studies published in the last 6 years, from 2016 to 2021, have been selected. The selection process is represented through the Prisma flow chart. It has been identified that these increasing challenges of healthcare can be overcome by the implication of AI, ML, DL, Edge AI, IoMT, 6G, and cloud computing. Still, limited areas have implemented these latest advancements and also experienced improvements in the outcomes. These implications have shown successful results not only in resolving the issues from the perspective of the patient but also from the perspective of healthcare professionals. It has been recommended that the different models that have been proposed in several studies must be validated further and implemented in different domains, to validate the effectiveness of these models and to ensure that these models can be implemented in several regions effectively.

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New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

Smart parking systems are a crucial component of the “smart city” concept, especially in the age of the Internet of Things (IoT). They aim to take the stress out of finding a vacant parking spot in city centers, due to the increasing number of cars, especially during peak hours. To realize the concept of smart parking, IoT-enabling technologies must be utilized, as the traditional way of developing smart parking solutions entails a lack of scalability, compatibility with IoT-constrained devices, security, and privacy awareness. In this paper, we propose a secure and privacy-preserving framework for smart parking systems. The framework relies on the publish/subscribe communication model for exchanging a huge volume of data with a large number of clients. On one hand, it provides functional services, including parking vacancy detection, real-time information for drivers about parking availability, driver guidance, and parking reservation. On the other hand, it provides security approaches on both the network and application layers. In addition, it supports mutual authentication mechanisms between entities to ensure device/ data authenticity, and provide security protection for users. That makes our proposed framework resilient to various types of security attacks, such as replay, phishing, and man-in-the-middle attacks. Finally, we analyze the performance of our framework, which is suitable for IoT devices, in terms of computation and network overhead.

/pdf/secsps-a-secure-and-privacy-preserving-framework-for-smart-3vyh62g192.pdf

Ibrahim Alrashdi

Papers

AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning

FBAD: Fog-based Attack Detection for IoT Healthcare in Smart Cities

Blockchain and Fog Computing in IoT-Driven Healthcare Services for Smart Cities

New Opportunities, Challenges, and Applications of Edge-AI for Connected Healthcare in Internet of Medical Things for Smart Cities

SecSPS: A Secure and Privacy-Preserving Framework for Smart Parking Systems