Interpretable intrusion detection for next generation of Internet of Things

Djenouri, Youcef; Belhadi, Asma; Srivastava, Gautam; Lin, Jerry Chun‐Wei; Yazidi, Anis

doi:10.1016/j.comcom.2023.03.005

Cited by 5 publications

(2 citation statements)

References 34 publications

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“…The intelligent network needs a low-latency network. The increasing IoT in smart 52 for intrusion detection in the next-generation Internet of Things. But, the time was not minimized.…”

Section: Proposed Methodology a System Modelmentioning

confidence: 99%

“…Lastly, network traffic samples are precisely classified for attack detection in 6G with higher attack detection accuracy and minimal false positive rate. Architecture and details of the steps of working, Algorithm, and Flowchart of the proposed methodology Data preprocessing A new framework named Interpretable Recurrent Neural Network (IRNN) was developed in Djenouri et al 52 for intrusion detection in the next‐generation Internet of Things. But, the time was not minimized.…”

Section: Proposed Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

Bhuvaneshwari¹,

Balusamy

Dhanaraj

et al. 2023

Int J Communication

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SummaryRegardless of the developments of networking and communication technologies, security is without exception a predominant feature to ensure network reliability. The future sixth‐generation (6G) network is anticipated to be carried out with artificial intelligence (AI) powered communication via machine learning (ML), post‐quantum cryptography, and so on. AI‐powered communication has been in recent years utilized in enhancing network traffic performance with respect to resource management, optimal frequency spectrum design, security, and latency. The studies of modern wireless communications and anticipated features of 6G networks revealed a prerequisite for designing a trustworthy attack detection mechanism. In this work, a method called, Luong Attention and Hosmer Lemeshow Regression Window‐based (LA‐HLRW) attack detection in 6G is proposed. Initially, with the raw Botnet Attack dataset obtained as input, preprocessing is performed to normalize network traffic features. Next, the dimensionality of network traffic feature of large‐scale network traffic data is reduced using the Luong Attention integrated with Long Short Term Memory (LSTM)‐based Feature extraction model. Finally, with the objective of classifying network traffic samples for attack detection in 6G, we analyze the low dimensional network traffic feature set produced by Luong Attention integrated with LSTM using the Hosmer Lemeshow Logistic Regression Window‐based Attack Detection model. Extensive experiments are performed with the Botnet Attack dataset to validate the efficiency of the proposed LA‐HLRW method by using different parameters such as attack detection accuracy, attack detection time, precision, and recall. The overall analysis of proposed LA‐HLRW results significantly reduced the attack detection time by 24%, and additionally improved attack detection accuracy, precision, and recall by 5%, 5%, and 6% as compared to existing attack detection methods respectively.

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“…The intelligent network needs a low-latency network. The increasing IoT in smart 52 for intrusion detection in the next-generation Internet of Things. But, the time was not minimized.…”

Section: Proposed Methodology a System Modelmentioning

confidence: 99%