Papers › Deep Learning Applications for Intrusion Detection in Network Traffic
Deep Learning Applications for Intrusion Detection in Network Traffic
Aleksandr Getman, Maxim Goryunov, Andrey Matskevich, Dmitry Rybolovlev, Anastasiya Nikolskaya
The paper discusses the issues of applying deep learning methods for detecting computer attacks in network traffic. The results of the analysis of relevant studies and reviews of deep learning applications for intrusion detection are presented. The most used deep learning methods are discussed and compared. The classification system of deep learning methods for intrusion detection is proposed. Current trends and challenges of applying deep learning methods for detecting computer attacks in network traffic are identified. The CNN-BiLSTM neural network is synthesized to assess the applicability of deep learning methods for intrusion detection. The synthesized neural network is compared to the previously developed model based on the use of the Random Forest classifier. The usage of the deep learning method enabled to simplify the feature engineering stage, and evaluation metrics of Random Forest and CNN-BiLSTM models are close. This confirms the prospects for the application of deep learning methods for intrusion detection.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Network Intrusion Detection | CICIDS2017 | CNN-BiLSTM | Avg F1 | 0.908 | #4 of 5 | Archive leaderboard | report |
| Network Intrusion Detection | CICIDS2017 | CNN-BiLSTM | Precision | 95.9 | #4 of 5 | Archive leaderboard | report |
| Network Intrusion Detection | CICIDS2017 | CNN-BiLSTM | Recall | 86.2 | #4 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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