GöwFed

Belenguer, Aitor; Pascual, Jose A.; Navaridas, Javier

doi:10.1016/j.jnca.2023.103653

Cited by 8 publications

(2 citation statements)

References 23 publications

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“…Performance metrics in machine learning are used to evaluate how well a model or algorithm performs and how effective it is. Performance metrics quantitatively assess various performance measures of a model, such as accuracy, precision, recall, F1 score and training time [62], [63], [64]. The selection of these metrics reflects our goal to evaluate algorithms' performance and optimize decision-making processes in security sensitive IoT environments, considering both accuracy and speed.…”

Section: B Performance Metricsmentioning

confidence: 99%

A Comprehensive Analysis of the Machine Learning Algorithms in IoT IDS Systems

Ozdogan

2024

IEEE Access

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In this study, we comprehensively compare machine learning algorithms in IoT IDS (Internet of Things Intrusion Detection System) systems from various aspects. We evaluate accuracy, precision, and training time. We examine the effects of data preprocessing techniques including normalization, outlier removal, standardization, and regularization on the datasets. Furthermore, we investigate the impact of dataset balancing, considering both balanced and imbalanced scenarios, on machine learning performance. We also analyzed the contribution of feature selection on the four different datasets. Based on our findings, we observe that certain preprocessing operations provide significant advantages in various ML algorithms, whereas others have very low impact, and their performance varies depending on the dataset and feature selection. The aim of this study is to facilitate the complexity and lengthiness of machine learning processes and algorithm selection, providing insights for future academic research. By addressing this objective, we aim to shed light on simplifying the utilization of machine learning algorithms. The study addresses the challenges arising from the complexity of machine learning processes in IoT IDS systems. This contribution can greatly benefit researchers in their academic endeavors. This multifaceted approach proves beneficial when comparing the methods under consideration, fostering a scientific discourse on their efficacy within contexts.

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Section: B Performance Metricsmentioning

confidence: 99%

A Comprehensive Analysis of the Machine Learning Algorithms in IoT IDS Systems

Ozdogan

2024

IEEE Access

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“…To increase the importance of essential devices in the learning process, [29] developed a federated learning technique with an attention mechanism. The number of communication cycles was decreased as a result of the introduced model, which effectively gave devices of larger relevance more weight.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Intrusion Detection Through Federated Learning With Enhanced Ghost_BiNet and Homomorphic Encryption

ChandraUmakantham,

Gajendran,

Marappan

2024

IEEE Access

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This research article proposes a federated learning-based intrusion detection methodology that leverages Enhanced Ghost_BiNet, a novel deep learning model, to enhance the security of information sharing and detection accuracy. Federated learning, a privacy-preserving machine learning technique, is utilized to enable multiple entities to collaboratively train a global intrusion detection model without sharing sensitive data. The proposed system first trains local models using Enhanced Ghost_BiNet, which integrates GhostNet and Bidirectional Gated Recurrent Unit (BiGRU). To optimize the model's performance, the Chaotic Chebyshev Artificial Humming Bird (CAh) algorithm is employed. Homomorphic encryption is applied to encrypt the local model updates, enhancing data privacy and security. Server-side aggregation of updates and collaborative optimization are introduced to minimize communication rounds during data aggregation. The results demonstrate that the Enhanced Ghost_BiNet outperforms traditional models like GhostNet, BiGRU, RNN, Auto Encoder, and CNN in terms of accuracy, precision, recall, F-Score, and mean square error (MSE). For instance, the Enhanced Ghost_BiNet achieves an accuracy of 99.24% on the KDD CUP 99 dataset, surpassing the other models by a significant margin. The proposed methodology provides a robust and secure approach to intrusion detection, ensuring the confidentiality of sensitive data while improving detection accuracy.

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