Internet of Things Enabled by Artificial Intelligence

Dixit, Rinku Sharma; Choudhary, Shailee Lohmor

doi:10.1201/9781003056751-10

Cited by 2 publications

(1 citation statement)

References 1 publication

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“…The high utilization of IoT devices faces IoT security issues in the fast expansion of smart appliances because of connecting to the network. The device-linked IoT devices consist of home automation; thermostats, printers, refrigerators, etc., are operated with the help of artificial intelligence like Google Assistant, Amazon Alexa [9,10]. Hence, hijacking [11] these devices are easy by sending spam emails, conscripted into a botnet, and privacy leaks.…”

Section: Introductionmentioning

confidence: 99%

Threat Analysis in IOT Network Using Evolutionary Sparse Convolute Network Intrusion Detection System

Raheema¹

2023

Int. J. Onl. Eng.

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Internet of Things (IoT) played a crucial role in various sectors such as automobiles and the logistic tracking medical field because it consists of distributed nodes, servers, and software for effective communication. Although, this IoT paradigm suffered from intrusion threats and attacks that cause security and privacy issues. Existing intrusion detection techniques fail to maintain reliability against the attacks. Therefore, in this work, IoT intrusion threat has been analyzed by using the sparse convolute network to contest the threats and attacks. The network is trained using sets of intrusion data, characteristics, and suspicious activities, which helps identify and track the attacks, mainly Distributed Denial of Service (DDoS) attacks. Along with this, the network is optimized using evolutionary techniques that identify and detect the regular, error, and intrusion attempts under different conditions. The sparse network forms the complex hypotheses evaluated using neurons, and the obtained event stream outputs are propagated to further hidden layer processes. This process minimizes the intrusion involvement in IoT data transmission. The effective utilization of training patterns in the network classifies the standard and threat patterns successfully. Then the effectiveness of the system is evaluated using experimental results and discussion.

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Section: Introductionmentioning

confidence: 99%

Threat Analysis in IOT Network Using Evolutionary Sparse Convolute Network Intrusion Detection System

Raheema¹

2023

Int. J. Onl. Eng.

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Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China

Wang

Zhao²,

Gangadhari

et al. 2021

Sustainability

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Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation management, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars’ work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key challenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established. The results of the study indicated that “lack of infrastructure (C1)”, ”insufficient funds (C2)”, “cybersecurity risks (C3)”, and “lack of trust in AI, IoT” are the causal factors that are slowing down the adoption of AI and IoT in smart city development. The inter-relationships between the various challenges are presented using a network relationship map, cause–effect diagram. The study’s findings can help regulatory bodies, policymakers, and researchers to make better decisions to overcome the challenges for developing sustainable smart cities.

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Internet of Things Enabled by Artificial Intelligence

Cited by 2 publications

References 1 publication

Threat Analysis in IOT Network Using Evolutionary Sparse Convolute Network Intrusion Detection System

Threat Analysis in IOT Network Using Evolutionary Sparse Convolute Network Intrusion Detection System

Analyzing the Adoption Challenges of the Internet of Things (IoT) and Artificial Intelligence (AI) for Smart Cities in China

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