Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

Alotaibi, Yazeed; Ilyas, Mohammad

doi:10.3390/s23125568

Cited by 29 publications

(7 citation statements)

References 64 publications

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“…It is worth noting that the ensemble model by itself represents a model-based modification defense mechanism. However, HopSkipJump showed the extreme weakness of the ensemble method, and further investigation of better algorithm selection is needed [ 42 , 54 ]. Obviously, the robustness and vulnerability of models vary based on the deployed algorithm, attack, and dataset types.…”

Section: Methodsmentioning

confidence: 99%

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

Alkadi,

Al-Ahmadi,

Ben Ismail

2024

Sensors

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Recently, Machine Learning (ML)-based solutions have been widely adopted to tackle the wide range of security challenges that have affected the progress of the Internet of Things (IoT) in various domains. Despite the reported promising results, the ML-based Intrusion Detection System (IDS) proved to be vulnerable to adversarial examples, which pose an increasing threat. In fact, attackers employ Adversarial Machine Learning (AML) to cause severe performance degradation and thereby evade detection systems. This promoted the need for reliable defense strategies to handle performance and ensure secure networks. This work introduces RobEns, a robust ensemble framework that aims at: (i) exploiting state-of-the-art ML-based models alongside ensemble models for IDSs in the IoT network; (ii) investigating the impact of evasion AML attacks against the provided models within a black-box scenario; and (iii) evaluating the robustness of the considered models after deploying relevant defense methods. In particular, four typical AML attacks are considered to investigate six ML-based IDSs using three benchmarking datasets. Moreover, multi-class classification scenarios are designed to assess the performance of each attack type. The experiments indicated a drastic drop in detection accuracy for some attempts. To harden the IDS even further, two defense mechanisms were derived from both data-based and model-based methods. Specifically, these methods relied on feature squeezing as well as adversarial training defense strategies. They yielded promising results, enhanced robustness, and maintained standard accuracy in the presence or absence of adversaries. The obtained results proved the efficiency of the proposed framework in robustifying IDS performance within the IoT context. In particular, the accuracy reached 100% for black-box attack scenarios while preserving the accuracy in the absence of attacks as well.

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Section: Methodsmentioning

confidence: 99%

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

Alkadi,

Al-Ahmadi,

Ben Ismail

2024

Sensors

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“…Sensors 2024, 24, x FOR PEER REVIEW 5 of 21 framework based on the architecture demonstrated better results compared to other methods in terms of having a distributed architecture outperforming a centralized type and yielding detection and classification accuracy rates that cause a reduction of limitations on cloud-based services due to employing the fog layer on the architecture. An ensemble approach was proposed by the authors in [22] for IoT networks to be implemented by the IDS to identify and analyze the network for any malicious patterns. The objective is to enhance the IDS's effectiveness and performance in detecting attacks and classifying network traffic as normal and abnormal.…”

Section: Architecture Of Iomtmentioning

confidence: 99%

Security Analysis for Smart Healthcare Systems

Ibrahim,

Al-Wadi,

Elhafiz

2024

Sensors

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The healthcare industry went through reformation by integrating the Internet of Medical Things (IoMT) to enable data harnessing by transmission mediums from different devices, about patients to healthcare staff devices, for further analysis through cloud-based servers for proper diagnosis of patients, yielding efficient and accurate results. However, IoMT technology is accompanied by a set of drawbacks in terms of security risks and vulnerabilities, such as violating and exposing patients’ sensitive and confidential data. Further, the network traffic data is prone to interception attacks caused by a wireless type of communication and alteration of data, which could cause unwanted outcomes. The advocated scheme provides insight into a robust Intrusion Detection System (IDS) for IoMT networks. It leverages a honeypot to divert attackers away from critical systems, reducing the attack surface. Additionally, the IDS employs an ensemble method combining Logistic Regression and K-Nearest Neighbor algorithms. This approach harnesses the strengths of both algorithms to improve attack detection accuracy and robustness. This work analyzes the impact, performance, accuracy, and precision outcomes of the used model on two IoMT-related datasets which contain multiple attack types such as Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS). The yielded results showed that the proposed ensemble method was effective in detecting intrusion attempts and classifying them as attacks or normal network traffic, with a high accuracy of 92.5% for the first dataset and 99.54% for the second dataset and a precision of 96.74% for the first dataset and 99.228% for the second dataset.

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“…The authors in [24] emphasized that ensemble learning (EL) significantly contributed to the improved performance of their experiment. They detailed an EL approach that combines four supervised ML algorithms: Decision Tree, Random Forest, Logistic Regression, and K-Nearest Neighbor, employing two ensemble techniques.…”

Section: Ensemble Learningmentioning

confidence: 99%

An Ensemble-Based Multi-Classification Machine Learning Classifiers Approach to Detect Multiple Classes of Cyberbullying

Alqahtani,

Ilyas

2024

MAKE

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The impact of communication through social media is currently considered a significant social issue. This issue can lead to inappropriate behavior using social media, which is referred to as cyberbullying. Automated systems are capable of efficiently identifying cyberbullying and performing sentiment analysis on social media platforms. This study focuses on enhancing a system to detect six types of cyberbullying tweets. Employing multi-classification algorithms on a cyberbullying dataset, our approach achieved high accuracy, particularly with the TF-IDF (bigram) feature extraction. Our experiment achieved high performance compared with that stated for previous experiments on the same dataset. Two ensemble machine learning methods, employing the N-gram with TF-IDF feature-extraction technique, demonstrated superior performance in classification. Three popular multi-classification algorithms: Decision Trees, Random Forest, and XGBoost, were combined into two varied ensemble methods separately. These ensemble classifiers demonstrated superior performance compared to traditional machine learning classifier models. The stacking classifier reached 90.71% accuracy and the voting classifier 90.44%. The results of the experiments showed that the framework can detect six different types of cyberbullying more efficiently, with an accuracy rate of 0.9071.

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Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

Cited by 29 publications

References 64 publications

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

RobEns: Robust Ensemble Adversarial Machine Learning Framework for Securing IoT Traffic

Security Analysis for Smart Healthcare Systems

An Ensemble-Based Multi-Classification Machine Learning Classifiers Approach to Detect Multiple Classes of Cyberbullying

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