A novel analytic approach for insider and outsider attack detection and classification using the dual-tree complex wavelet transform and convolutional neural networks
Bibliographic record
Abstract
This paper presents a novel approach to detect and classify both insider and outsider attacks targeting smart digital substation automation systems (SD-SASs). An Intrusion Detection System (IDS) based upon the Dual-Tree Complex Wavelet Transform (DT-CWT) and deep learning is used to detect outsider attacks, which are initiated by unauthorized actors, and insider attacks, which are performed by authorized users who have legitimate access to SD-SASs. In this work, both physical and cyber data are represented in a joint time-frequency complex wavelet domain to enable the extraction of the key features of both insider and outsider attacks. The presented approach is analytic as it uses complex wavelets that preserve the information of both physical and cyber features in both the real and imaginary components of the wavelet coefficients instead of only using the real-valued coefficients for feature representation as in real transforms. The resulting IDS is experimentally tested on collected real-time data using OPAL-RT and the results have demonstrated its effectiveness in detecting both outsider and insider attacks at an accuracy of 99.00 %. The use of the DT-CWT improves the detection accuracy compared to other approaches and enables the classification of such attacks from other power disturbances.
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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 | 0.000 |
| 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".