Intrusion Detection for Smart Home Security Based on Data Augmentation with Edge Computing

Yuan, Danni; Ota, Kaoru; Dong, Mianxiong; Zhu, Xiaoyan; Wu, Tao; Zhang, Linjie; Ma, Jianfeng

doi:10.1109/icc40277.2020.9148632

Cited by 28 publications

(12 citation statements)

References 19 publications

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“…While there are multiple ways to create training data, including Generative Adversarial Net (GAN) and SVM Feature selection, many studies suggest creating training data using the same datasets that are labeled together. These methods focus on creating new training datasets based on the same dataset [57][58][59][60][61]. Nonetheless, our proposal focuses on transferring data from both the same and different datasets using three scenarios.…”

Section: Comparison Of Methods Of Creating Training Datamentioning

confidence: 99%

PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

2021

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The number of studies on applying machine learning to cyber security has increased over the past few years. These studies, however, are facing difficulties with making themselves usable in the real world, mainly due to the lack of training data and reusability of a created model. While transfer learning seems like a solution to these problems, the number of studies in the field of intrusion detection is still insufficient. Therefore, this study proposes payload feature-based transfer learning as a solution to the lack of training data when applying machine learning to intrusion detection by using the knowledge from an already known domain. Firstly, it expands the extracting range of information from header to payload to accurately deliver the information by using an effective hybrid feature extraction method. Secondly, this study provides an improved optimization method for the extracted features to create a labeled dataset for a target domain. This proposal was validated on publicly available datasets, using three distinctive scenarios, and the results confirmed its usability in practice by increasing the accuracy of the training data created from the transfer learning by 30%, compared to that of the non-transfer learning method. In addition, we showed that this approach can help in identifying previously unknown attacks and reusing models from different domains.

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Section: Comparison Of Methods Of Creating Training Datamentioning

confidence: 99%

PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

2021

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“…Their tests show that the proposed approach is effective and robust in detecting abnormal traffic in all of its variations. Yuan et al [25] proposed a technique for converting network traffic data to images and detecting anomalous traffic using CNN. The trained classifier would be put on smart home edge nodes.…”

Section: Related Workmentioning

confidence: 99%

An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices

Idrissi¹,

Azizi²,

Moussaoui³

2022

IJEECS

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Machine learning (ML) and deep learning (DL) have achieved amazing progress in diverse disciplines. One of the most efficient approaches is unsupervised learning (UL), a sort of algorithms for analyzing and clustering unlabeled data; it allows identifying hidden patterns or performing data clustering over provided data without the need for human involvement. There is no prior knowledge of actual abnormalities when using UL methods in anomaly detection (AD); hence, a DL-intrusion detection system (IDS)- based on AD depends intensely on their assumption about the distribution of anomalies. In this paper, we propose a novel unsupervised AD Host-IDS for internet of things (IoT) based on adversarial training architecture using the generative adversarial network (GAN). Our proposed IDS, called “EdgeIDS”, targets mostly IoT devices because of their limited functionality; IoT devices send and receive only specific data, not like traditional devices, such as servers or computers that exchange a wide range of data. We benchmarked our proposed “EdgeIDS” on the message queuing telemetry transport (MQTTset) dataset with five attack types, and our obtained results are promising, up to 0.99 in the ROC-AUC metric, and to just 0.035 in the ROC-EER metric. Our proposed technique could be a solution for detecting cyber abnormalities in the IoT.

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“…In order to better illustrate the advantages of the proposed algorithm, the training models of the comparison federal learning methods are CNN. Reasonable selection of hyperparameters will greatly affect the performance of the algorithm [30]. Under different hyperparameter configurations, we studied the classification performance of the centralized learning model CNN (CL-CNN) and the FL model and determined the reasonable parameters of the algorithms (the specific parameter configuration is shown in the table).…”

Section: Applicability Of Fl Modelmentioning

confidence: 99%

Intelligent Intrusion Detection Based on Federated Learning for Edge-Assisted Internet of Things

Man

Zeng

Yang

et al. 2021

Security and Communication Networks

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As an innovative strategy, edge computing has been considered a viable option to address the limitations of cloud computing in supporting the Internet-of-Things applications. However, due to the instability of the network and the increase of the attack surfaces, the security in edge-assisted IoT needs to be better guaranteed. In this paper, we propose an intelligent intrusion detection mechanism, FedACNN, which completes the intrusion detection task by assisting the deep learning model CNN through the federated learning mechanism. In order to alleviate the communication delay limit of federal learning, we innovatively integrate the attention mechanism, and the FedACNN can achieve ideal accuracy with a 50% reduction of communication rounds.

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Intrusion Detection for Smart Home Security Based on Data Augmentation with Edge Computing

Cited by 28 publications

References 19 publications

PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

PF-TL: Payload Feature-Based Transfer Learning for Dealing with the Lack of Training Data

An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices

Intelligent Intrusion Detection Based on Federated Learning for Edge-Assisted Internet of Things

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