A Network Intrusion Detection System using Deep Learning against MQTT Attacks in IoT

Mosaiyebzadeh, Fatemeh; Rodriguez, Luis Gustavo Araujo; Batista, Daniel Macêdo; Hirata, Roberto

doi:10.1109/latincom53176.2021.9647850

Cited by 14 publications

(20 citation statements)

References 13 publications

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“…Mosaiyebzadeh et al [31] proposed a model for NIDS DL based on IoT networks that combines three techniques: CNN, RNN, and LSTM. An experiment was conducted on the MQQ-IoT2020 dataset, and the average accuracy was 97%.…”

Section: Literature Reviewmentioning

confidence: 99%

Enhancing IoT Security: A Few-Shot Learning Approach for Intrusion Detection

Althiyabi,

Ahmad,

Alassafi

2024

Mathematics

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Recently, the number of Internet of Things (IoT)-connected devices has increased daily. Consequently, cybersecurity challenges have increased due to the natural diversity of the IoT, limited hardware resources, and limited security capabilities. Intrusion detection systems (IDSs) play a substantial role in securing IoT networks. Several researchers have focused on machine learning (ML) and deep learning (DL) to develop intrusion detection techniques. Although ML is good for classification, other methods perform better in feature transformation. However, at the level of accuracy, both learning techniques have their own certain compromises. Although IDSs based on ML and DL methods can achieve a high detection rate, the performance depends on the training dataset size. Incidentally, collecting a large amount of data is one of the main drawbacks that limits performance when training datasets are lacking, and such methods can fail to detect novel attacks. Few-shot learning (FSL) is an emerging approach that is employed in different domains because of its proven ability to learn from a few training samples. Although numerous studies have addressed the issues of IDSs and improved IDS performance, the literature on FSL-based IDSs is scarce. Therefore, an investigation is required to explore the performance of FSL in IoT IDSs. This work proposes an IoT intrusion detection model based on a convolutional neural network as a feature extractor and a prototypical network as an FSL classifier. The empirical results were analyzed and compared with those of recent intrusion detection approaches. The accuracy results reached 99.44%, which shows a promising direction for involving FSL in IoT IDSs.

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Section: Literature Reviewmentioning

confidence: 99%

Enhancing IoT Security: A Few-Shot Learning Approach for Intrusion Detection

Althiyabi,

Ahmad,

Alassafi

2024

Mathematics

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“…Several research groups have proposed deep learning for network intrusion detection in recent years with very good results [1][2][3][4][5][6][7][8][9][10][11]36]. A survey by Ullah et al [7] already analyzed 37 papers published between 2017 and 2021 regarding DLIDS.…”

Section: Dlidsmentioning

confidence: 99%

“…A survey by Ullah et al [7] already analyzed 37 papers published between 2017 and 2021 regarding DLIDS. Some research also addresses IoT-related attacks targeting the MQTT messaging protocol [1,2,[4][5][6]36].…”

Section: Dlidsmentioning

confidence: 99%

“…Over the past years, machine learning techniques, and in particular deep neural networks, have attracted great interest in the field of network intrusion detection. Several publications exist that show the benefit of this approach [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…A survey from 2021 lists about 54 different intrusion detection systems based on machine learning that address the IoT domain [17]. In particular, the use of deep learning IDS (DLIDS) is especially favored in the literature [1][2][3][4][5][6][9][10][11]. The term deep learning is used for neural networks that consist of at least four layers [18].…”

Section: Introductionmentioning

confidence: 99%

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A Comparison of Neural-Network-Based Intrusion Detection against Signature-Based Detection in IoT Networks

Schrötter,

Niemann,

Schnor

2024

Information

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Over the last few years, a plethora of papers presenting machine-learning-based approaches for intrusion detection have been published. However, the majority of those papers do not compare their results with a proper baseline of a signature-based intrusion detection system, thus violating good machine learning practices. In order to evaluate the pros and cons of the machine-learning-based approach, we replicated a research study that uses a deep neural network model for intrusion detection. The results of our replicated research study expose several systematic problems with the used datasets and evaluation methods. In our experiments, a signature-based intrusion detection system with a minimal setup was able to outperform the tested model even under small traffic changes. Testing the replicated neural network on a new dataset recorded in the same environment with the same attacks using the same tools showed that the accuracy of the neural network dropped to 54%. Furthermore, the often-claimed advantage of being able to detect zero-day attacks could not be seen in our experiments.

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