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How effectiveness of hybrid detection systems tin IoBT networks?

Intrusion detection system

Naive Bayes classifier

Constant false alarm rate

False positive rate

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The effectiveness of hybrid detection systems in IoT networks, particularly in combating cyber threats, is well-documented in the literature. Various studies propose hybrid models combining machine learning techniques like Naive Bayes, K-NN, and ensemble methods to enhance the accuracy and efficiency of Intrusion Detection Systems (IDS) in IoT environments . These hybrid systems aim to address the challenges posed by diverse cyberattacks, including botnet attacks and Denial of Service (DoS) attacks, by leveraging a combination of anomaly-based and signature-based detection mechanisms . By integrating feature selection methods, such as wrapper methods and filter-based approaches, these hybrid systems optimize the detection accuracy while minimizing computational resources, ultimately improving the overall performance of IDS in IoT networks .

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Proceedings Article•DOI Hybrid Machine Learning based Intrusion Detection System for IoT 04 Nov 2022	The hybrid machine learning IDS proposed in the research enhances IoBT network security, achieving 91.8% accuracy in multiclass and 96.2% in binary classification, outperforming individual models.
Open access•Posted Content•DOI Machine Learning based Hybrid Intrusion Detection System for detecting Cross-layer DoS attacks in IoT 13 Apr 2023	The effectiveness of the Hybrid IDS in IoT networks is demonstrated by detecting Cross-Layer DoS attacks with up to 96% accuracy, reduced false positives, and efficient testing times.
Open access•Posted Content•DOI A Hybrid Intrusion Detection System against Botnet Attack in IoT using Light Weight Signature and Ensemble Learning Technique 23 Jun 2022	The hybrid detection system in IoBT networks effectively detects botnet attacks with low false positives and high accuracy rates, as demonstrated in the proposed model.
Proceedings Article•DOI Hybrid Machine Learning based Intrusion Detection System for IoT Kajol Mittal, Payal Khurana Batra - Show less +1 more 04 Nov 2022	The hybrid machine learning IDS proposed in the research enhances IoBT network security, achieving 91.8% accuracy in multiclass and 96.2% in binary classification, outperforming individual models.
Proceedings Article•DOI Hybrid Feature Selection Models for Machine Learning Based Botnet Detection in IoT Networks Alejandro Guerra-Manzanares, Hayretdin Bahsi, Sven Nõmm - Show less +2 more 01 Oct 2019 30 Citations	Hybrid feature selection models enhance botnet detection effectiveness in IoT networks by reducing computational load without sacrificing accuracy, as demonstrated in the study.

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