Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities

Ahmed, Shamsuddin; Hossain, Md. Farhad; Kaiser, M. Shamim; Noor, Manan Binth Taj; Mahmud, Mufti; Chakraborty, Chinmay

doi:10.1007/978-3-030-72139-8_2

Cited by 35 publications

(8 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, in contrast to our contribution, the proposal of [38] paid attention to the security issues that may threaten the SS in addition to existing privacy policies without considering ML solutions and IoT role. Besides, the work presented in [39] addressed the security issues in SS applications with no detailed analysis for the community giving a general presentation without a clear contribution about the secure IoT-based SS using ML.…”

Section: A Motivationmentioning

confidence: 99%

Toward Secured IoT-Based Smart Systems Using Machine Learning

et al. 2023

View full text Add to dashboard Cite

Machine learning (ML) and the internet of things (IoT) are among the most booming research directions. Smart cities, smart campuses (SCs), smart homes, smart cars, early warning systems (EWSs), etc.; or it could be called ''Smart x'' systems are implemented using ML and IoT. Those systems will alter how various world entities communicate with one another. This paper spots light on the significant roles of the IoT in SS. Also, it focuses on the importance of ML in IoT-based SS. Besides, an overview of smartness and IoT is presented. Then, this paper offers ML benchmarking along with a taxonomy that categorizes the ML models into linear and non-linear ones depending on the problem type (classification or regression). Afterward, the commonly utilized evaluation metrics are provided. In addition, this paper considers the trust techniques used for mitigating different security aspects in IoT networks, which play a crucial part in regulating the new era of communication. Moreover, two case studies devoting ML for IoT-based SS, namely IoT-based SC and IoT-based EWS, are considered for data collection and manipulation with guided research directions. Finally, the paper presents effective recommendations of ML's significant roles in SC and earthquake EWS for interested scholars.INDEX TERMS Machine learning, Internet of Things, smart systems, security.

show abstract

Section: A Motivationmentioning

confidence: 99%

Toward Secured IoT-Based Smart Systems Using Machine Learning

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The network traffic of smart cities through IoT systems has seen exponential growth and presents novel cybersecurity difficulties as these loT devices were linked to sensors that can be straightforwardly linked to enormous cloud servers. To mitigate such attacks, developers should advance novel methods to detect infected IoT gadgets [5]. The cyber threats could acquire unauthorized access to loT gadgets without the knowledge of the administrator or user (for example Miria botnet) [6].…”

Section: Fig 1 Overview Of Iot For Smart Citiesmentioning

confidence: 99%

Deep Learning and Metaheuristics based Cyber Threat Detection in Internet of Things Enabled Smart City Environment

Das,

Manchala,

Rout

et al. 2023

Preprint

View full text Add to dashboard Cite

Recently, the extensive use of Internet of Things (IoT) applications has a stronger impact and greater contribution to the development of the smart city. A smart city (SC) uses IoT-based technologies, applications, and communications for maximizing operational efficacy and improving the service quality of providers and the living standard of people. With the development of SC networks, there also comes the augmented menace of cybersecurity attacks and threats. IoT gadgets within an SC network were linked to sensors connected to huge cloud servers and are vulnerable to malicious threats and attacks. Therefore, it is significant to formulate techniques for preventing such assaults and protecting IoT gadgets from failures. This article develops a new transient search algorithm with optimal stacked sparse autoencoder (TSA-OSSAE) based cyber threat detection in IoT-enabled SC applications. The presented TSA-OSSAE technique majorly focuses on the recognition of cyber threats to attain security in the SC. To attain this, the projected TSA-OSSAE system follows TSA based feature selection approach to reduce computational complexity. Besides, the TSA-OSSAE technique applies the SSAE model for cyber threat detection. At last, the hyperparameters of the SSAE approach are optimally chosen by utilizing of multi-versus optimizer (MVO) algorithm. The experimental result analysis of the TSA-OSSAE technique was performed by using the TON_IoT telemetry database. The simulation outcomes signify the promising performance of the TSA-OSSAE methodology over other existing techniques.

show abstract

“…The answer for these issues is to tweak the customary organization plans to the most recent guidelines of organization insight, which guarantees ideal security. Medical clinics, centres and care offices need cost effective network, the security of which consents to information insurance guidelines but at the same time is simple to utilize and to work [14].…”

Section: Iot and Healthcarementioning

confidence: 99%

“…Once ingested, they can do certain jobs straightforwardly in the patient's body. Right when the prescription is conveyed, the development separates and is handled, a decision that is great for enormous, long, chained drug particles like proteins, peptides and antibodies [14].…”

Section: Digital Drugsmentioning

confidence: 99%

Artificial Intelligence in Smart cities and Healthcare

Sowmitha¹,

S.²,

Harshini³

et al. 2022

EAI Endorsed Trans Smart Cities

View full text Add to dashboard Cite

In the era of the Internet of things IoT and Artificial knowledge (AI) continues to define the century.Artificial intelligence refers to a wide term that incorporates machine learning, normal language handling, rule based expert systems, actual robots, and robotic automation . The rise of computerized system and clinical gadgets in securely and productively diagnosing, treating, and planning care is an obvious sign that AI is digging in for the long haul and fill in significance. While AI holds a great deal of potential, the ramifications for essential consideration suppliers should be tended to as it might restrict execution. Since the Epidemic cities in 2019, the healthcare industry has escalated its AI-based advances by 60%. As indicated by the investigation, AI calculations like ANN, RNN/LSTM, CNN/R-CNN, DNN, and SVM/LS-SVMbhas a higher impact on the different smart city domains. Smart city advances the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To achieve this, smart cities require coordinated effort among the general public as well as private sectors to introduce IT stages to gather and examine massive amounts of information. Simultaneously, it is vital to design effective artificial intelligence (AI) based tools to deal with medical services emergency circumstances in smart urban communities. This paper reviews about the current technologies like artificial intelligence in the healthcare for smart cities.

show abstract

Artificial Intelligence and Machine Learning for Ensuring Security in Smart Cities

Cited by 35 publications

References 41 publications

Toward Secured IoT-Based Smart Systems Using Machine Learning

Toward Secured IoT-Based Smart Systems Using Machine Learning

Deep Learning and Metaheuristics based Cyber Threat Detection in Internet of Things Enabled Smart City Environment

Artificial Intelligence in Smart cities and Healthcare

Contact Info

Product

Resources

About