DFE: efficient IoT network intrusion detection using deep feature extraction

Basati, Amir; Faghih, Mohammad Mehdi

doi:10.1007/s00521-021-06826-6

Cited by 18 publications

(2 citation statements)

References 42 publications

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“…Their algorithm worked well in wireless sensor networks. Basmati [19] et al proposed a lightweight intrusion detection model based on the node characteristics of network attacks, which can solve the problem of insufficient computing power of some nodes in the industrial Internet. Hua [20] et al desgin Network Attack identification method for industrial control networks based on the RNN-GBRBM feature decode, which is based on the manual selection of features in the original data packets, followed by the osPCA measurement to identify the traffic features, and the detection of the traffic at last.…”

Section: Related Workmentioning

confidence: 99%

Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Lan

Huang

et al. 2023

Preprint

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Considering the robustness and accuracy of conventional intrusion detection models are easily influenced by adversarial attacks, this work proposes an anti-attack intrusion detection algorithm based on a message-passing neural network with traffic spatiotemporal features. Our algorithm can not only effectively distinguish and correlate upstream and downstream traffics, but also clearly embody the relationship between different traffics from the same source node and destination node by the established graph structure. We also improve the model by utilising the characteristics of the existing network attacks, which indicates thatthe traffic spatiotemporal characteristics could achieve high accuracy on attack traffic detection. Compared to the previous algorithms, extensive experiments show that our proposed model outperforms other similar models in performance. For the multi-classification tasks, our algorithm can effectively detect most of the network attacks and outperform traditional machine learning methods. In terms of algorithm robustness, our algorithm is less affected by adversarial attacks than traditional machine learning algorithms.

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Section: Related Workmentioning

confidence: 99%

Anti-attack Intrusion Detection Model based on MPNN and Traffic Spatiotemporal Characteristics

Lan

Huang

et al. 2023

Preprint

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“…Several researchers have proposed different techniques for the detection of malicious attacks in IoT networks. Basati et al [ 19 ] presented an IDS called deep feature extraction (DFE). This model is based on a CNN.…”

Section: Related Workmentioning

confidence: 99%

A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering

Ullah

Ahmad

Khan

et al. 2022

Sensors

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The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The performance analysis of the proposed model was carried out with several metrics, such as accuracy, precision, recall, and F1-score. A number of optimization techniques were applied to the proposed model in which Adam, AdaMax, and Nadam performance was optimum. In addition, the proposed model was compared with various advanced deep learning (DL) and traditional machine learning (ML) techniques. All experimental analysis indicates that the accuracy of the proposed approach is high and more robust than existing DL-based algorithms.

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