Ensemble learning-based IDS for sensors telemetry data in IoT networks

Naz, Naila; Khan, Muazzam A.; Alsuhibany, Suliman A.; Diyan, Muhammad; Tan, Zhiyuan; Khan, Muhammad Almas; Ahmad, Jawad

doi:10.3934/mbe.2022493

Cited by 13 publications

(16 citation statements)

References 68 publications

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“…While IDS deals with the network’s behavior in terms of attacks, it is established to learn from this behavior pattern using several techniques. Thus, the system will recognize any violated network traffic that can lead to an attack from this pattern [ 42 , 43 ].…”

Section: Related Workmentioning

confidence: 99%

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

Alotaibi

Ilyas

2023

Sensors

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The Internet of Things (IoT) comprises a network of interconnected nodes constantly communicating, exchanging, and transferring data over various network protocols. Studies have shown that these protocols pose a severe threat (Cyber-attacks) to the security of data transmitted due to their ease of exploitation. In this research, we aim to contribute to the literature by improving the Intrusion Detection System (IDS) detection efficiency. In order to improve the efficiency of the IDS, a binary classification of normal and abnormal IoT traffic is constructed to enhance the IDS performance. Our method employs various supervised ML algorithms and ensemble classifiers. The proposed model was trained on TON-IoT network traffic datasets. Four of the trained ML-supervised models have achieved the highest accurate outcomes; Random Forest, Decision Tree, Logistic Regression, and K-Nearest Neighbor. These four classifiers are fed to two ensemble approaches: voting and stacking. The ensemble approaches were evaluated using the evaluation metrics and compared for their efficacy on this classification problem. The accuracy of the ensemble classifiers was higher than that of the individual models. This improvement can be attributed to ensemble learning strategies that leverage diverse learning mechanisms with varying capabilities. By combining these strategies, we were able to enhance the reliability of our predictions while reducing the occurrence of classification errors. The experimental results show that the framework can improve the efficiency of the Intrusion Detection System, achieving an accuracy rate of 0.9863.

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Section: Related Workmentioning

confidence: 99%

“…An IDS is an acronym for an intrusion detection system, which is focused on behavior detection to handle anomalies or attacks [ 42 ]. While IDS deals with the network’s behavior in terms of attacks, it is established to learn from this behavior pattern using several techniques.…”

Section: Background Studiesmentioning

confidence: 99%

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

Alotaibi

Ilyas

2023

Sensors

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“…The tool of choice for learning binary-type algorithms is logistic regression. With a binomial reaction variable, it really goes much further than linear regression [19]. The ability to use explanatory variables continuously and the simplicity of addressing many explanatory variables at once are also advantages.…”

Section: Logistic Regression (Lr)mentioning

confidence: 99%

Real-time human activity recognition from smart phone using linear support vector machines

Maaloul¹,

Lejdel²,

Abdelhamid³

2023

TELKOMNIKA

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The recognition of human activity (HAR) the use of cell devices embedded in its exten sively disbursed sensors affords guidance, instructions, and take care of citizens of smart cities. Consequently, it became essential to analyze human every day sports. To examine statistical models of human conduct, synthetic intelligence strategies such as machine studying can be used. Many studies have not studied type overall performance in real-time due to statistics series. To remedy this trouble, this paper proposes a structure primarily based on open supply technology and platforms consisting of Apache Kafka, for messages to flow over the internet, method them and provide shape for existing facts in real-time and formulates the trouble of identifying human pastime by using a smartphone tool as a type hassle using statistics collection by telephone sensors. The proposed version is skilled by some machine learning algorithms. The algorithm that has proven superior and quality results helps a linear vector machines.

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“…To detect malicious DoH traffic, the system may need to implement specialized tools and techniques, such as deep packet inspection [ 18 , 19 ], behavioral analysis [ 20 , 21 ], or machine learning algorithms [ 13 , 22 ]. Additionally, it is important to stay up-to-date with the latest threats and vulnerabilities related to DoH and to follow best practices for securing your network and endpoints [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

“…Malicious DNS over HTTPS (DoH) is a type of cyberattack that involves using encrypted DNS over HTTPS to bypass traditional network security measures and send DNS queries to a malicious server controlled by an attacker. Machine learning can improve the effectiveness of malicious DoH attacks by creating more sophisticated and evasive attacks [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach

Al‐Haija

Alohaly

Odeh

2023

Sensors

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The Domain Name System (DNS) protocol essentially translates domain names to IP addresses, enabling browsers to load and utilize Internet resources. Despite its major role, DNS is vulnerable to various security loopholes that attackers have continually abused. Therefore, delivering secure DNS traffic has become challenging since attackers use advanced and fast malicious information-stealing approaches. To overcome DNS vulnerabilities, the DNS over HTTPS (DoH) protocol was introduced to improve the security of the DNS protocol by encrypting the DNS traffic and communicating it over a covert network channel. This paper proposes a lightweight, double-stage scheme to identify malicious DoH traffic using a hybrid learning approach. The system comprises two layers. At the first layer, the traffic is examined using random fine trees (RF) and identified as DoH traffic or non-DoH traffic. At the second layer, the DoH traffic is further investigated using Adaboost trees (ADT) and identified as benign DoH or malicious DoH. Specifically, the proposed system is lightweight since it works with the least number of features (using only six out of thirty-three features) selected using principal component analysis (PCA) and minimizes the number of samples produced using a random under-sampling (RUS) approach. The experiential evaluation reported a high-performance system with a predictive accuracy of 99.4% and 100% and a predictive overhead of 0.83 µs and 2.27 µs for layer one and layer two, respectively. Hence, the reported results are superior and surpass existing models, given that our proposed model uses only 18% of the feature set and 17% of the sample set, distributed in balanced classes.

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Ensemble learning-based IDS for sensors telemetry data in IoT networks

Cited by 13 publications

References 68 publications

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

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

Real-time human activity recognition from smart phone using linear support vector machines

A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach

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