Sequence to Sequence Pattern Learning Algorithm for Real-Time Anomaly Detection in Network Traffic
Bibliographic record
Abstract
Network intrusions can be modeled as anomalies in network traffic in which the expected order of packets and their attributes deviate from regular traffic. Algorithms that predict the next sequence of events based on previous sequences are a promising avenue for detecting such anomalies. In this paper, we present a novel multi-attribute model for predicting a network packet sequence based on previous packets using a sequence-to-sequence (Seq2Seq) encoder-decoder model. This model is trained on an attack-free dataset to learn the normal sequence of packets in TCP connections and then it is used to detect anomalous packets in TCP traffic. We show that in DARPA 1999 dataset, the proposed multi-attribute Seq2Seq model detects anomalous raw TCP packets which are part of intrusions with 97 % accuracy. Also, it can detect selected intrusions in real-time with 100% accuracy and outperforms existing algorithms based on recurrent neural network models such as LSTM.
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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".