Network Intrusion Detection System Based on Separable Convolution AutoEncoder with Long Short-Term Memory
Bibliographic record
Abstract
In recent years, Intrusion Detection System (IDS) that continuously detects, analyzed network traffic for signs of malicious activity by enabling prompt detection and response to potential security incidents. Traditional approaches IDS had faced several challenges which include high false positive rates, limited detection capability. Therefore, this research proposes Separable Convolution AutoEncoder-Long Short-Term Memory (SCAE-LSTM) for network intrusion detection system. Initially, data is taken from Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS) 2018 dataset is preprocessed by using t-Distributed Stochastic Neighboring Embedding (t-SNE) which reduces effectively in high-dimensional network traffic data to a lower dimensional representation. Then, the features are extracted from preprocessed data by using Local Linear Embedding (LLE) which identified nonlinear structures and anomalies that indicated in the network intrusions. After that, classification is done by using SCAE to complex patterns and anomalies in traffic data. Finally, the network intrusions are detected by using LSTM which effectively improves accuracy, reduces false positives, enhances the overall robustness in intrusion detection system. The proposed SCAE-LSTM achieved better accuracy (0.9755), detection rate (0.9999), F1 value (0.9575) and false positive rate (0.0128) when compared with existing CNN-LSTM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".