Machine Learning for Detecting Anomalies and Intrusions in Communication Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cyber attacks are becoming more sophisticated and, hence, more difficult to detect. Using efficient and effective machine learning techniques to detect network anomalies and intrusions is an important aspect of cyber security. A variety of machine learning models have been employed to help detect malicious intentions of network users. In this paper, we evaluate performance of recurrent neural networks (Long Short-Term Memory and Gated Recurrent Unit) and Broad Learning System with its extensions to classify known network intrusions. We propose two BLS-based algorithms with and without incremental learning. The algorithms may be used to develop generalized models by using various subsets of input data and expanding the network structure. The models are trained and tested using Border Gateway Protocol routing records as well as network connection records from the NSL-KDD and Canadian Institute of Cybersecurity datasets. Performance of the models is evaluated based on selected features, accuracy, F-Score, and training time.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it