Summary of Scgnet-stacked Convolution with Gated Recurrent Unit Network For Cyber Network Intrusion Detection and Intrusion Type Classification, by Rajana Akter et al.
SCGNet-Stacked Convolution with Gated Recurrent Unit Network for Cyber Network Intrusion Detection and Intrusion Type Classification
by Rajana Akter, Shahnure Rabib, Rahul Deb Mohalder, Laboni Paul, Ferdous Bin Ali
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel deep learning architecture called SCGNet is proposed in this study, which demonstrates promising results on the NSL-KDD dataset in both network attack detection and attack type classification tasks. The SCGNet consists of a stacked convolutional neural network with a gated recurrent unit, enabling it to effectively identify complex and varied network attacks. Traditional IDSs are limited by their inability to quickly and efficiently identify these types of attacks, especially those linked to low-frequency attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Network attack detection is crucial for protecting hosts or networks from malicious activity. This study proposes a novel deep learning architecture called SCGNet that can detect network attacks with high accuracy. The SCGNet was tested on the NSL-KDD dataset and achieved 99.76% accuracy in detecting attacks, as well as 98.92% accuracy in classifying attack types. |
Keywords
» Artificial intelligence » Classification » Deep learning » Neural network
