Mitigating False Negative intruder decisions in WSN-based Smart Grid monitoring
Bibliographic record
Abstract
Monitoring the Smart Grid (SG) is highly desired for critical applications such as power quality assessment and transformer monitoring. Due to their low-cost, flexibility and efficiency as well as their widely usage in several critical infrastructure monitoring applications, Wireless Sensor Networks (WSNs) are estimated to be extensively used in SG applications. WSNs-based SG networks are vulnerable to different types of attacks and intruders. In order to operate networks in secured environments, in this paper we analyze our Clustered Hierarchal Hybrid-Intrusion Detection System (CHH-IDS) that is responsible for various attacks injected by known and unknown intruders. As False Positives (FPs) and False Negatives (FNs) are the key performance parameters in IDS, we investigate mitigation of FNs through a two-tier intrusion detection approach, which deals with anomaly and signature detection in parallel. In the presence of such a hybrid mode, utilization proportion between the anomaly detection and signature detection models affect the FN performance. In these two subsystems, Random Forest method is used for signature detection over known attacks and E-DBSCAN (Enhanced Density-Based Spatial Clustering of Applications with Noise) method is used for anomaly detection over unknown attacks. Through simulations that run on real datasets, we validate that the higher the weight of anomaly detection subsystem (i.e. the lower the weight of the signature detection subsystem), the lower the FN rates experienced by the entire H-IDS system. More specifically, we show that FN rates can be significantly reduced by 20.4% when the weight on anomaly detection subsystem is increased from 60% to 70% while the accuracy is expected to be improved through signature detection subsystem by using the Random Forest which has higher detection rate than the E-DBSCAN method.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".