Intelligent Detection of DDoS Attack in IoT Network

Gahelot, Parul; Sarangi, Pradeepta Kumar; Rani, Lekha

doi:10.1007/978-981-19-7982-8_15

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…This review addresses the limitation of the available DL techniques used for cyber-attack detection in IoT environments [13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Most of the recent available studies provide DL systems with high percentages of attack detection accuracy.…”

Section: Discussionmentioning

confidence: 99%

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A Review on IoT Cyber-Attacks Detection Challenges‎ and Solutions

2023

IJSER

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The Internet of Things (IoT) has recast the way we interact with technology and the world around us. The IoT is almost everywhere in our daily lives, and this huge growth in using the IoT increases the need for implementing a high level of security framework for this technology. This study presents an analysis of some of the recent IoT cyber-attack detection systems to provide an evaluation of these systems and useful future research directions in this field. This analysis is introduced along with an overview of the IoT's security challenges and solutions and the types of security attacks in the IoT environment. Although the most recent approaches to cyber-attack detection have high percentages of attack detection accuracy, these classical DL models that were learned with local datasets need more enhancement to maintain privacy and data storage when involved in networks of cooperative nodes in IoTs. Furthermore, current techniques still lack the ability to provide a generalization for new attacks, cover the binary and multiclass classifications of cyber-attacks, and develop feature selection algorithms to reduce dataset dimension.

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Section: Discussionmentioning

confidence: 99%

“…Parul Gahelot, et al [24] (2023) presented an intelligent method of detecting DDoS attacks on IoT networks. Authors proposed a CNN model in their study for detecting DDoS attack.…”

Section: Another Study Bymentioning

confidence: 99%

A Review on IoT Cyber-Attacks Detection Challenges‎ and Solutions

2023

IJSER

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“…Detection Technique Features 2019 [134] Hybrid Learning 2020 [130,135] Neural Network 2023 [133,136] CNN 2023 [132] Ensemble Learning 2023 [136] LSTM…”

Section: Year(s) Paper(s)mentioning

confidence: 99%

“…Some common approaches are listed in Table 12 . The paper [ 133 ] uses the CNN model trained using a dataset containing benign and DDoS attack packets. To validate the model, various validation methods such as cross-validation, subsampling, and repeated cross-validation are employed on novel labeled datasets.…”

Section: Iot Botnet Detectionmentioning

confidence: 99%

Systematic Literature Review of IoT Botnet DDOS Attacks and Evaluation of Detection Techniques

Gelgi,

Guan,

Arunachala

et al. 2024

Sensors

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Internet of Things (IoT) technology has become an inevitable part of our daily lives. With the increase in usage of IoT Devices, manufacturers continuously develop IoT technology. However, the security of IoT devices is left behind in those developments due to cost, size, and computational power limitations. Since these IoT devices are connected to the Internet and have low security levels, one of the main risks of these devices is being compromised by malicious malware and becoming part of IoT botnets. IoT botnets are used for launching different types of large-scale attacks including Distributed Denial-of-Service (DDoS) attacks. These attacks are continuously evolving, and researchers have conducted numerous analyses and studies in this area to narrow security vulnerabilities. This paper systematically reviews the prominent literature on IoT botnet DDoS attacks and detection techniques. Architecture IoT botnet DDoS attacks, evaluations of those attacks, and systematically categorized detection techniques are discussed in detail. The paper presents current threats and detection techniques, and some open research questions are recommended for future studies in this field.

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