An efficient intrusion detection system for MQTT-IoT using enhanced chaotic salp swarm algorithm and LightGBM

Prajisha, C.; Vasudevan, A. R.

doi:10.1007/s10207-022-00611-9

Cited by 16 publications

(7 citation statements)

References 36 publications

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“…Building on the theme of enhancing MQTT security, both Prajisha et al [93] and Alaiz et al [8] explored efficient IDSs and multiclass classification procedures, respectively, to identify and classify attacks on the MQTT-IoT protocol. Both works demonstrate how ML techniques can be applied to enhance security in MQTT communication.…”

Section: Ml-based Techniquesmentioning

confidence: 99%

A survey on IoT application layer protocols, security challenges, and the role of explainable AI in IoT (XAIoT)

Quincozes,

Kazienko

et al. 2024

Int. J. Inf. Secur.

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The Internet of Things (IoT) plays a fundamental role in contemporary society, necessitating an in-depth comprehension of its application layer protocols, intertwined technologies, security issues, and effective countermeasures. This survey delivers an exhaustive analysis of these protocols, emphasizing the escalating significance of Explainable Artificial Intelligence in IoT (XAIoT). To elucidate its practical implications, we conducted a case study examining a real-world scenario where XAIoT significantly bolstered IoT security. This case study demonstrated XAIoT's potential to enhance transparency and trustworthiness in IoT systems. Furthermore, the survey critically evaluates existing literature, pinpointing specific opportunities and gaps in the present state of IoT application layer security. For instance, our analysis revealed a pressing need for more robust security protocols and the integration of advanced machine learning techniques for anomaly detection in IoT applications. This survey, designed to provide a comprehensive perspective, seeks to stimulate additional innovation and research in the realm of secure and intelligent IoT applications. In doing so, it contributes to the ongoing dialogue on improving IoT security, offering valuable insights for researchers and practitioners alike.

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Section: Ml-based Techniquesmentioning

confidence: 99%

A survey on IoT application layer protocols, security challenges, and the role of explainable AI in IoT (XAIoT)

Quincozes,

Kazienko

et al. 2024

Int. J. Inf. Secur.

View full text Add to dashboard Cite

show abstract

“…In order to find the optimal segmentation variable j and segmentation point s, traverse all variables j and scan all segmentation points for each segmentation variable j to find the optimal segmentation point s. Construct a (j, s) and then find the optimal (j, s) among the entire (j, s) [17,18]. According to the principle of minimizing square error, the optimal (j, s) can be obtained by using the minimum loss function formula (3).…”

Section: Lightgbm Feature Algorithm Theorymentioning

confidence: 99%

Design of Industrial IoT Intrusion Security Detection System Based on LightGBM Feature Algorithm and Multi-layer Perception Network

Deng

2024

JCSANDM

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Continuous improvement of machine learning technology has provided more support for intrusion security detection in the industrial Internet of Things (IoT). The intrusion security detection system based on LightGBM feature algorithm and multi-layer perception network fusion provides more options for improving security detection, further enhancing the effectiveness of industrial IoT intrusion security detection. By comparing the applications of different models in the security detection process, the model constructed based on the LightGBM feature algorithm can achieve higher accuracy and precision in industrial IoT intrusion security detection, as well as higher F1-score and AUC values; A more reasonable detection time also lays the foundation for improving the overall efficiency of industrial IoT intrusion security detection. Therefore, in the field of industrial IoT intrusion security detection, the detection model constructed in this article can provide more support for further improvement and improvement of IoT intrusion security detection performance.

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“…Prajisha et al [27] presented an efficient IDS that combines the chaotic SALP swarm algorithm with LightGBM and achieved outstanding accuracy on a variety of datasets. The accuracy of ECSSA-LightGBM is 99.38% for MC-IoT; that of ECSSA-LightGBM is 98.91% for MQTT-IoT-IDS2020; and that of ECSSA-LightGBM is 98.35% for MQTTset.…”

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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An efficient intrusion detection system for MQTT-IoT using enhanced chaotic salp swarm algorithm and LightGBM

Cited by 16 publications

References 36 publications

A survey on IoT application layer protocols, security challenges, and the role of explainable AI in IoT (XAIoT)

A survey on IoT application layer protocols, security challenges, and the role of explainable AI in IoT (XAIoT)

Design of Industrial IoT Intrusion Security Detection System Based on LightGBM Feature Algorithm and Multi-layer Perception Network

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

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