Toward Secured IoT-Based Smart Systems Using Machine Learning

Abdalzaher, Mohamed S.; Fouda, Mostafa M.; El-Sayed, Hesham; Salim, Mahmoud M.

doi:10.1109/access.2023.3250235

Cited by 11 publications

(2 citation statements)

References 112 publications

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“…Additionally, this emphasises the value of ML in IoT-based SS. Additionally, a summary of smarts and IoT is provided [16].…”

Section: Literature Surveymentioning

confidence: 99%

Accurate and Efficient Security Authentication of IoT Devices using Machine Learning Algorithms

Alghamdi,

Alzahrani

2024

Networks, Blockchain and Internet of Things

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The rapid proliferation of Internet of Things (IoT) devices has led to an increase in botnet attacks targeting these devices. A botnet attack is a cyber-attack in which a network of compromised devices, referred to as "bots" or "zombies," is utilized to execute a synchronized attack. These attacks can result in substantial harm to both the devices and the network to which they are connected. This study investigates the deployment of security authentication protocols to verify the identity of IoT devices prior to network connection. The study also evaluates the classification accuracy of four distinct supervised machine learning algorithms: Random Forest (RF), Naïve Bayes (NB), DecisionTree (DT), and eXtreme Gradient Boosting (XGBoost). It was foundXGBoost was the best performing classifier among the various machine learning algorithms tested, in terms of detecting botnet attacks in IoT networks using the Bot-IoT dataset.

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“…Additionally, this emphasises the value of ML in IoT-based SS. Additionally, a summary of smarts and IoT is provided [16].…”

Section: Literature Surveymentioning

confidence: 99%

Accurate and Efficient Security Authentication of IoT Devices using Machine Learning Algorithms

Alghamdi,

Alzahrani

2024

Networks, Blockchain and Internet of Things

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“…The random forests (RF) approach is an effective machine learning ensemble method that may be used for classi cation and regression applications. It combines the predictions of numerous models, each made up of decision trees, to generate more accurate predictions (Abdalzaher et al, 2023). Randomness is introduced on two levels, the rst of which is random sampling, and the second is random feature selection.…”

Section: Random Forestsmentioning

confidence: 99%

Anomaly Detection of Distributed Denial of Service (DDoS) in IoT Network Using Machine Learning

Mohammed,

Sallehudin,

Safie

et al. 2023

Preprint

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This research focuses on developing an anomaly detection system using machine learning to mitigate Distributed Denial of Service (DDoS) attacks in IoT networks. The study utilizes a diverse dataset from IoT environments to train and evaluate machine learning algorithms for DDoS detection. The dataset includes various IoT device types, communication protocols, and network configurations. The research aims to achieve several objectives, including dataset preprocessing, feature engineering, machine learning model selection, anomaly detection, and performance evaluation. The research team preprocesses the raw Internet of Things (IoT) network data by cleaning and transforming it to prepare it for analysis. They then extract relevant features from the data to effectively characterize normal and abnormal network behavior. Multiple machine learning algorithms are evaluated and compared to determine the most suitable models for DDoS detection in IoT networks. The selected machine learning models are then used to identify and classify abnormal traffic patterns associated with DDoS attacks. The performance of the developed anomaly detection system is evaluated by assessing its accuracy, precision, recall, and F1 score. The significance of this research lies in its potential to enhance the security of IoT networks by proactively detecting and mitigating DDoS attacks. By leveraging machine learning, the study aims to provide a robust defense mechanism against this pervasive threat, ensuring the reliability and availability of IoT services and applications.

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A Novel Intelligence System for Hybrid Crop Suitable Landform Prediction Using Machine Learning Techniques and IoT

Senthil,

Prabha,

Sridevi

et al. 2024

Artificial Intelligence and Sustainable Computing

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Toward Secured IoT-Based Smart Systems Using Machine Learning

Cited by 11 publications

References 112 publications

Accurate and Efficient Security Authentication of IoT Devices using Machine Learning Algorithms

Accurate and Efficient Security Authentication of IoT Devices using Machine Learning Algorithms

Anomaly Detection of Distributed Denial of Service (DDoS) in IoT Network Using Machine Learning

A Novel Intelligence System for Hybrid Crop Suitable Landform Prediction Using Machine Learning Techniques and IoT

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