Anomalous behavior detection-based approach for authenticating smart home system users

Amraoui, Noureddine; Zouari, Belhassen

doi:10.1007/s10207-021-00571-6

Cited by 6 publications

(4 citation statements)

References 44 publications

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“…By default, this is called binary logistic regression. Another type of logistic regression is multinomial logistic regression, which is used for solving the problems of multi-class classification [28]. However, multinomial logistic regression is an extension of traditional logistic regression [26], but this method can handle more than two feasible discrete outcomes.…”

Section: ) Logistic Regressionmentioning

confidence: 99%

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

et al. 2023

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Machine learning (ML) provides effective solutions to develop efficient intrusion detection system (IDS) for various environments. In the present paper, a diversified study of various ensemble machine learning (ML) algorithms has been carried out to propose design of an effective and time-efficient IDS for Internet of Things (IoT) enabled environment. In this paper, data captured from network traffic and real-time sensors of the IoT-enabled smart environment has been analyzed to classify and predict various types of network attacks. The performance of Logistic Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine classifiers have been benchmarked using an open-source largely imbalanced dataset 'DS2OS' that consists of 'normal' and 'anomalous' network traffic. An intrusion detection model ''LGB-IDS'' has been proposed using the LGBM library of ML after validating its superiority over other algorithms using ensemble techniques and on the basis of majority voting. The performance of the proposed intrusion detection system is suitably validated using certain performance metrics of machine learning such as train and test accuracy, time efficiency, error-rate, true-positive rate (TPR), and false-negative rate (FNR). The experimental results reveal that XGB and LGBM have almost equal accuracy, but the time efficiency of LGBM is much better than RF, and XGB classifiers. The main objective of the present paper is to propose a design of an efficient intrusion detection model with high accuracy, better time efficiency, and reduced false alarm rate. The experimental results show that the proposed model achieves an accuracy of 99.92% and the time efficiency comes to be much higher than other prevalent algorithms-based models. The threat detection rate is greater than 90% and less than 100%. Time complexity of LGBM is also very much low as compared to other ML algorithms.INDEX TERMS Machine learning classification algorithms, ensemble classifiers, gradient boosting algorithms, light gradient boosting machines (LGBM), and intrusion detection systems (IDS).Open Access funding provided by 'Università degli Studi di Enna "KORE"' within the CRUI CARE Agreement

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Section: ) Logistic Regressionmentioning

confidence: 99%

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

et al. 2023

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“…In the first case, Naïve Bayes had the lowest success rate, while in the second case, they achieved the highest prediction rates using Decision Tree and Random Forest. Amroui and Zouari [10] have proposed an architecture called Duenna to detect user behaviors that exhibit different behaviors, taking into account the use of devices within smart home systems by regular users. In this way, they have helped increase security against malicious individuals who threaten smart-home users and want to hijack the systems.…”

Section: Introductionmentioning

confidence: 99%

IoT-based Smart Home Security System with Machine Learning Models

HIZAL,

ÇAVUŞOĞLU,

AKGÜN

2024

Academic Platform Journal of Engineering and Smart Systems

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The Internet of Things (IoT) has various applications in practice, such as smart homes and buildings, traffic management, industrial management, and smart farming. On the other hand, security issues are raised by the growing use of IoT applications. Researchers develop machine learning models that focus on better classification accuracy and decreasing model response time to solve this security problem. In this study, we made a comparative evaluation of machine learning algorithms for intrusion detection systems on IoT networks using the DS2oS dataset. The dataset was first processed to feature extraction using the info gain attribute evaluation feature extraction approach. The original dataset (12 attributes), the dataset (6 attributes) produced using the info gain approach, and the dataset (11 attributes) obtained by eliminating the timestamp attribute was then formed. These datasets were subjected to performance testing using several machine learning methods and test choices (crossfold-10, percentage split). The test performance results are presented, and an evaluation is performed, such as accuracy, precision, recall, and F1 score. According to the test results, it has been observed that high accuracy detection rates are achieved for IoT devices with limited processing power.

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“…Every device operates in a specific pattern. If a user tries to operate any device differently, that results in a change in the normal behavior of that device, and the modified behavior is considered as malicious [6]. In such situations, the current behavior is matched with the historical behavior of the specific device to verify the mode of behavior (whether safe or unsafe).…”

Section: Introductionmentioning

confidence: 99%

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

Rani

Gill

Gulia

et al. 2022

Computational Intelligence and Neuroscience

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Internet of Things (IoT) is the fastest growing technology that has applications in various domains such as healthcare, transportation. It interconnects trillions of smart devices through the Internet. A secure network is the basic necessity of the Internet of Things. Due to the increasing rate of interconnected and remotely accessible smart devices, more and more cybersecurity issues are being witnessed among cyber-physical systems. A perfect intrusion detection system (IDS) can probably identify various cybersecurity issues and their sources. In this article, using various telemetry datasets of different Internet of Things scenarios, we exhibit that external users can access the IoT devices and infer the victim user’s activity by sniffing the network traffic. Further, the article presents the performance of various bagging and boosting ensemble decision tree techniques of machine learning in the design of an efficient IDS. Most of the previous IDSs just focused on good accuracy and ignored the execution speed that must be improved to optimize the performance of an ID model. Most of the earlier pieces of research focused on binary classification. This study attempts to evaluate the performance of various ensemble machine learning multiclass classification algorithms by deploying on openly available “TON-IoT” datasets of IoT and Industrial IoT (IIoT) sensors.

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Anomalous behavior detection-based approach for authenticating smart home system users

Cited by 6 publications

References 44 publications

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

Design of an Intrusion Detection Model for IoT-Enabled Smart Home

IoT-based Smart Home Security System with Machine Learning Models

An Ensemble-Based Multiclass Classifier for Intrusion Detection Using Internet of Things

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