Intrusion Detection in 5G Cellular Network Using Machine Learning

Mahmood, Ishtiaque; Alyas, Tahir; Abbas, Sagheer; Shahzad, Tariq; Abbas, Qaiser; Ouahada, Khmaies

doi:10.32604/csse.2023.033842

Cited by 2 publications

(1 citation statement)

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“…Researchers underscored the necessity of identifying cyber threats and employing machine learning (ML) methods to mitigate risks to the 5G network. Ishtiaque et al [67] addressed unidentified and suspicious circumstances in 5G networks, employing various machine learning algorithms, with the Linear Regression algorithm yielding the best results, achieving 92.12% precision on test data and 92.13% on train data. Similarly, Manoj et al [68] compared different CNN and LSTM models for malware detection on 5G/6G devices, with Ankita et al [69] proposing a CNN-DMA network for application in 5G/6G infrastructures.…”

Section: Malware Detection On 5g/6g Infrastructurementioning

confidence: 99%

Securing Edge Devices: Malware Classification with Dual-Attention Deep Network

Alandjani

2024

Applied Sciences

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Detecting malware is a crucial defense mechanism against potential cyber-attacks. However, current methods illustrate significant limitations in achieving high performance while maintaining faster inference on edge devices. This study proposes a novel deep network with dual-attention feature refinement on a two-branch deep network to learn real-time malware detection on edge platforms. The proposed method introduces lightweight spatial-asymmetric attention for refining the extracted features of its backbone and multi-head attention to correlate learned features from the network branches. The experimental results show that the proposed method can significantly outperform existing methods in quantitative evaluation. In addition, this study also illustrates the practicability of a lightweight deep network on edge devices by optimizing and deploying the model directly on the actual edge hardware. The proposed optimization strategy achieves a frame rate of over 545 per second on low-power edge devices.

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Section: Malware Detection On 5g/6g Infrastructurementioning

confidence: 99%