Lightweight Cryptography and IDS for Edge 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
The rapid growth of edge computing in IoT, smart cities, and autonomous vehicle applications has created significant security concerns stemming from decentralized designs and limited resources. While traditional security solutions offer robust protection, they impose substantial computational overhead that compromises edge device performance. This study presents a novel hybrid security framework that integrates AES-128-GCM lightweight encryption with a dual-classifier machine learning-based intrusion detection system (IDS) using Random Forest and SVM algorithms. Our implementation on a Raspberry Pi testbed demonstrates superior performance compared to conventional approaches, achieving 95% threat detection accuracy with only a 3% false positive rate, while processing 15,000 packets per second. The hybrid system reduces per-packet latency to 40ms compared to 60 ms for traditional IDS and 120 ms for standard encryption methods. Performance evaluation shows the framework maintains high security standards while significantly reducing computational overhead and energy consumption on resource-constrained edge devices. These results indicate that our hybrid approach effectively balances security and performance requirements for edge computing environments, making it particularly suitable for real-time applications requiring rapid data processing at the network edge.
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 | 0.000 | 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 it