A deep learning approach for intrusion detection in Internet of Things using focal loss function

Siddique, A.B.; Manivannan, D.

doi:10.1016/j.iot.2023.100699

Cited by 40 publications

(24 citation statements)

References 59 publications

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“…In the future, we may see more research on how to effectively integrate multiple deep learning models, and future deep learning models may focus more on the ability of multimodal and cross-modal learning to understand better and utilise complex data. Furthermore, the use of datasets in IoT environments highlights the specialisation, and there will be more datasets used specifically for IoT environments such as Bot-IoT [83], [92][93][94], IoT-23 [78], [91], ToN-IoT [95], and so on.…”

Section: The Status and Trends Of Ids Development In The Iotmentioning

confidence: 99%

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

Liao,

Murah,

Hasan

et al. 2024

IEEE Access

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Internet of Things (IoT) is transforming how we live and work and its applications are widespread, spanning smart homes, industrial monitoring, smart cities, healthcare, agriculture, and retail Considering its wide range applications, it is vital to address the security challenges arising from the massive collection and transmission of user data by IoT devices. Intrusion detection systems (IDS) based on deep learning techniques offer new means and research directions for resolving IoT security issues. Deep learning models can process large volumes of data and extract complex patterns, making them generally more effective than traditional rule based IDSs. While deep learning techniques are gradually gaining popularity in IDS applications, current research lacks a comprehensive summary of deep learning-based IDS in the context of IoT. This paper provides an introduction to intrusion detection technologies, followed by a detailed comparison, analysis, and discussion of deep learning models, datasets, feature extraction and classifiers, data preprocessing techniques, and experimental design of the models. It also highlights the current challenges and issues associated with deep learning models and relevant techniques for IDS. Finally, it concludes by providing recommendations to assist researchers in this domain.

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Section: The Status and Trends Of Ids Development In The Iotmentioning

confidence: 99%

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

Liao,

Murah,

Hasan

et al. 2024

IEEE Access

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“…Focal Loss can be adjusted autonomously according to the different costs of classification of positive and negative samples, selectively increasing the weight of hard-to-distinguish samples and reducing the loss of easy-to-classify samples to improve the model performance [10] , as shown in Equation ( 3).…”

Section: Focal Lossmentioning

confidence: 99%

Research on network intrusion detection based on SMOTEENN and improved CatBoost algorithm

Huoming,

Yinhui,

Pinglan

et al. 2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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To address the problems of network intrusion data imbalance and prediction accuracy, a network intrusion detection model based on SMOTEENN ( Synthetic Minority Oversampling Technique Edited Nearest Neighbor ) and improved CatBoost algorithm is proposed, and SMOTEENN sampling is used to solve the data imbalance problem from the data level. The original CatBoost algorithm loss function is replaced with Focal Loss loss function to enhance the classification performance of CatBoost algorithm from the level of sample classification difficulty. And the Bayesian optimization algorithm is used to realize the CatBoost hyperparameter combination seeking. The network intrusion dataset NSL-KDD ( National Security Letter Knowledge Discovery in Database ) is input to the improved model and compared with other similar algorithms. The results show that the proposed method in this paper is optimal in terms of accuracy, F1-sacore, and AP value, To provide new ideas for network intrusion algorithm research.

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“…This algorithm aims to provide a practical and effective tool for enhancing the security and reliability of IoT applications and services. (5) The ROAST-IoT algorithm introduces a novel approach by combining the rangeoptimized attention convolutional scattered technique (ROAST) with machine learning components like scattered range feature selection (SRFS) and attention-based convolutional feed-forward network (ACFN). This unique combination aims to overcome the limitations of existing solutions and provide a fresh perspective on intrusion detection in IoT networks.…”

Section: Research Motivationsmentioning

confidence: 99%

“…The extensive use of the Internet makes network security an unavoidable concern. Due to the Internet of Things' potential applications in various human activities, various IoT-related studies have recently attracted interest in the academic community and industry [5]. With the decline in sensor prices, the rise of remote storage services, and the popularity of big data, the IoT is seen as a viable solution to raise people's quality of life.…”

Section: Introductionmentioning

confidence: 99%

ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks

Mahalingam,

Perumal,

Subburayalu

et al. 2023

Sensors

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The Internet of Things (IoT) has significantly benefited several businesses, but because of the volume and complexity of IoT systems, there are also new security issues. Intrusion detection systems (IDSs) guarantee both the security posture and defense against intrusions of IoT devices. IoT systems have recently utilized machine learning (ML) techniques widely for IDSs. The primary deficiencies in existing IoT security frameworks are their inadequate intrusion detection capabilities, significant latency, and prolonged processing time, leading to undesirable delays. To address these issues, this work proposes a novel range-optimized attention convolutional scattered technique (ROAST-IoT) to protect IoT networks from modern threats and intrusions. This system uses the scattered range feature selection (SRFS) model to choose the most crucial and trustworthy properties from the supplied intrusion data. After that, the attention-based convolutional feed-forward network (ACFN) technique is used to recognize the intrusion class. In addition, the loss function is estimated using the modified dingo optimization (MDO) algorithm to ensure the maximum accuracy of classifier. To evaluate and compare the performance of the proposed ROAST-IoT system, we have utilized popular intrusion datasets such as ToN-IoT, IoT-23, UNSW-NB 15, and Edge-IIoT. The analysis of the results shows that the proposed ROAST technique did better than all existing cutting-edge intrusion detection systems, with an accuracy of 99.15% on the IoT-23 dataset, 99.78% on the ToN-IoT dataset, 99.88% on the UNSW-NB 15 dataset, and 99.45% on the Edge-IIoT dataset. On average, the ROAST-IoT system achieved a high AUC-ROC of 0.998, demonstrating its capacity to distinguish between legitimate data and attack traffic. These results indicate that the ROAST-IoT algorithm effectively and reliably detects intrusion attacks mechanism against cyberattacks on IoT systems.

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A deep learning approach for intrusion detection in Internet of Things using focal loss function

Cited by 40 publications

References 59 publications

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

A Survey of Deep Learning Technologies for Intrusion Detection in Internet of Things

Research on network intrusion detection based on SMOTEENN and improved CatBoost algorithm

ROAST-IoT: A Novel Range-Optimized Attention Convolutional Scattered Technique for Intrusion Detection in IoT Networks

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