Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

Alrowais, Fadwa; Althahabi, Sami; Alotaibi, Saud S.; Mohamed, Abdullah; Hamza, Manar Ahmed; Marzouk, Radwa

doi:10.32604/csse.2023.030188

Cited by 12 publications

(6 citation statements)

References 20 publications

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“…where ω is the weight matrix, b is the bias term, Ф is the nonlinear transformation from an n-dimensional space to a higher-dimensional feature space, Γ is the cost function, ε is the allowable error, and C is a constant that determines the tradeoff between the minimal training error and minimal model complexity ||ω|| 2 . The vectors outside the ε-tube can be obtained by using the slack variables ξ i .…”

Section: Appendixmentioning

confidence: 99%

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A Multimodel Transfer-Learning-Based Car Price Prediction Model with an Automatic Fuzzy Logic Parameter Optimizer

Kuo¹,

Chen²,

Yau³

2023

Computer Systems Science and Engineering

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Cars are regarded as an indispensable means of transportation in Taiwan. Several studies have indicated that the automotive industry has witnessed remarkable advances and that the market of used cars has rapidly expanded. In this study, a price prediction system for used BMW cars was developed. Nine parameters of used cars, including their model, registration year, and transmission style, were analyzed. The data obtained were then divided into three subsets. The first subset was used to compare the results of each algorithm. The predicted values produced by the two algorithms with the most satisfactory results were used as the input of a fully connected neural network. The second subset was used with an optimization algorithm to modify the number of hidden layers in a fully connected neural network and modify the low, medium, and high parameters of the membership function (MF) to achieve model optimization. Finally, the third subset was used for the validation set during the prediction process. These three subsets were divided using k-fold cross-validation to avoid overfitting and selection bias. In conclusion, in this study, a model combining two optimal algorithms (i.e., random forest and k-nearest neighbors) with several optimization algorithms (i.e., gray wolf optimizer, multilayer perceptron, and MF) was successfully established. The prediction results obtained indicated a mean square error of 0.0978, a root-mean-square error of 0.3128, a mean absolute error of 0.1903, and a coefficient of determination of 0.9249.

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Section: Appendixmentioning

confidence: 99%

“…In this study, used car data were used to develop a used car price prediction system. Machine learning (ML) models [1][2][3][4] were used to enhance the learning ability. In addition, Fuzzy was also applied to help retrieve the features.…”

Section: Introductionmentioning

confidence: 99%

A Multimodel Transfer-Learning-Based Car Price Prediction Model with an Automatic Fuzzy Logic Parameter Optimizer

Kuo¹,

Chen²,

Yau³

2023

Computer Systems Science and Engineering

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“…In study [ 24 ], a novel MFO-RELM approach was presented for cyber-security threat detection and classification in the IoT platform. The proposed MFO-RELM approach achieves the effective detection of cybersecurity attacks that occur in the IoT platform.…”

Section: Literature Reviewmentioning

confidence: 99%

Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment

Khadidos

Alyoubi

et al. 2023

Sensors

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Internet of Things (IoT) enables day-to-day objects to connect with the Internet and transmit and receive data for meaningful purposes. Recently, IoT has resulted in many revolutions in all sectors. Nonetheless, security risks to IoT networks and devices are persistently disruptive due to the growth of Internet technology. Phishing becomes a common threat to Internet users, where the attacker aims to fraudulently extract confidential data of the system or user by using websites, fictitious emails, etc. Due to the dramatic growth in IoT devices, hackers target IoT gadgets, including smart cars, security cameras, and so on, and perpetrate phishing attacks to gain control over the vulnerable device for malicious purposes. These scams have been increasing and advancing over the last few years. To resolve these problems, this paper presents a binary Hunter–prey optimization with a machine learning-based phishing attack detection (BHPO-MLPAD) method in the IoT environment. The BHPO-MLPAD technique can find phishing attacks through feature selection and classification. In the presented BHPO-MLPAD technique, the BHPO algorithm primarily chooses an optimal subset of features. The cascaded forward neural network (CFNN) model is employed for phishing attack detection. To adjust the parameter values of the CFNN model, the variable step fruit fly optimization (VFFO) algorithm is utilized. The performance assessment of the BHPO-MLPAD method takes place on the benchmark dataset. The results inferred the betterment of the BHPO-MLPAD technique over compared approaches in different evaluation measures.

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“…Además, existen otras ICT que comparten el objetivo de generar accesibilidad eficiente a la información, como los recommender systems, que son herramientas cuya utilidad se basa en el filtrado de información relevante para facilitar la toma de decisiones y que han demostrado ser eficientes en temas de energía, salud y tráfico [29]. Con respecto a la recolección masiva de datos que el IoT produce puede ser utilizada de múltiples formas beneficiosas para el desarrollo de la sociedad, empero, representa un riesgo para los usuarios, por lo que el incremento en la utilización genera la necesidad de desarrollar a la par herramientas de ciberseguridad capaces de brindar protección [31].…”

Section: Acerca De Las Ictunclassified

Smart cities and sustainable development in Asia Pacific

Azabache Morán,

Cancino Ugarte,

Castillo Lopez

et al. 2023

Proceedings of the 3rd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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With the advancement of technology and the need for digitization, the development of smart cities is increasing, and the Asia Pacific region has become a global benchmark. These are cities that seek sustainability, aligned with the preservation of the environment and the improvement of the quality of life of its citizens. This article presents a systematic review of the literature on smart cities and sustainable development in Asia Pacific. First, concepts are explained in relation to renewable energy practices, the suitability of information and communication technologies (ICT), and urban mobility. Then, the various contexts where smart cities are developed are analyzed and compared; the trends that are implemented in relation to renewable energy; the technological advances that are achieved or developed to satisfy the diverse needs of each city; and the obstacles that have been overcome in relation to the current arrangement of urban mobility. Finally, the research trends of smart cities and sustainable development in the Asia Pacific region in the period 2010-2022 are determined, as well as the initiatives and strategies that have been implemented in the process of transformation to smart and sustainable cities.

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Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

Cited by 12 publications

References 20 publications

A Multimodel Transfer-Learning-Based Car Price Prediction Model with an Automatic Fuzzy Logic Parameter Optimizer

A Multimodel Transfer-Learning-Based Car Price Prediction Model with an Automatic Fuzzy Logic Parameter Optimizer

Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment

Smart cities and sustainable development in Asia Pacific

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