An intelligent authentication & intrusion detection system for securing advanced metering infrastructure
Bibliographic record
Abstract
Advanced Metering Infrastructure (AMI) forms a crucial part of smart grids by enabling real-time monitoring and two-way communication between consumers and utility providers. However, its reliance on wireless and multi-hop communication exposes it to spoofing and identity-based attacks. In this paper, we propose an Intelligent Authentication & Intrusion Detection System (IAIDS) for securing AMI, designed to authenticate smart meters (SMs) and continuously monitor the network to detect identity based attacks in real time. It’s a novel three-phase framework that leverages the spatial correlation of RSSI patterns and machine learning to detect identity attacks. First, IAIDS estimates the expected RSSI (ERSSI) values by leveraging physical and environmental models (weather and terrain), thereby eliminating the need for predefined signal profiles. This Phase effectively reduces false alerts by accounting for normal RSSI fluctuations caused by environmental variability. Second, it utilizes unsupervised outlier detection techniques to dynamically identify potential anomalies at the local level, without relying on static thresholds. This enables each SM to adaptively detect suspicious behavior, improving detection sensitivity. Third, IAIDS confirms the anomalies through cooperative classification among neighboring SMs, enhancing precision. Evaluated on real RSSI and weather datasets, IAIDS achieves high precision and recall. This approach ensures high detection sensitivity while maintaining a low false alarm rate, making it suitable for secure, reliable, and scalable deployments within AMI network.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".