Detecting Anomalous Network Traffic in IoT Networks

Hoang, Dang Hai; Nguyen, Ha Duong

doi:10.23919/icact.2019.8702032

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…PCA [28] is a common data dimension reduction method, which compresses highdimensional data by extracting the main feature vectors. PCA maps n-dimensional features to k-dimensions, where n > k, where k-dimensional features become the principal components.…”

Section: Pca-based Methodsmentioning

confidence: 99%

Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things

Zhang,

Zhang

et al. 2023

Future Internet

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Network traffic classification (NTC) has attracted great attention in many applications such as secure communications, intrusion detection systems. The existing NTC methods based on supervised learning rely on sufficient labeled datasets in the training phase, but for most traffic datasets, it is difficult to obtain label information in practical applications. Although unsupervised learning does not rely on labels, its classification accuracy is not high, and the number of data classes is difficult to determine. This paper proposes an unsupervised NTC method based on adversarial training and deep clustering with improved network traffic classification (NTC) and lower computational complexity in comparison with the traditional clustering algorithms. Here, the training process does not require data labels, which greatly reduce the computational complexity of the network traffic classification through pretraining. In the pretraining stage, an autoencoder (AE) is used to reduce the dimension of features and reduce the complexity of the initial high-dimensional network traffic data features. Moreover, we employ the adversarial training model and a deep clustering structure to further optimize the extracted features. The experimental results show that our proposed method has robust performance, with a multiclassification accuracy of 92.2%, which is suitable for classification with a large number of unlabeled data in actual application scenarios. This paper only focuses on breakthroughs in the algorithm stage, and future work can be focused on the deployment and adaptation in practical environments.

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Section: Pca-based Methodsmentioning

confidence: 99%

Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things

Zhang,

Zhang

et al. 2023

Future Internet

View full text Add to dashboard Cite

show abstract

“…The PCA technique transforms a large set of variables into a reduced set of features without losing much of the information. Various research works [159][160][161][162] used a combination of PCA with various classifiers to detect anomalies in IoT networks. -It produces a more robust and accurate output which is resistant to overfitting.…”

Section: Principle Component Analysis (Pca)mentioning

confidence: 99%

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

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The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

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Coarse Traffic Classification for High-Bandwidth Connections in a Computer Network Using Deep Learning Techniques

Bolanowski

Paszkiewicz

Rumak

2021

Complex, Intelligent and Software Intensive Systems

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Detecting Anomalous Network Traffic in IoT Networks

Cited by 9 publications

References 25 publications

Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things

Intelligent Unsupervised Network Traffic Classification Method Using Adversarial Training and Deep Clustering for Secure Internet of Things

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Coarse Traffic Classification for High-Bandwidth Connections in a Computer Network Using Deep Learning Techniques

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