Securing LoRaWAN in the AIoT Era: A Systematic Mapping Study and an MITRE-Based Threat Matrix
Bibliographic record
Abstract
The rapid expansion of the Internet of Things (IoT) has established LoRaWAN (Long Range Wide Area Network) as a leading low-power, long-range communication protocol across critical domains such as smart cities, agriculture, and healthcare. However, its minimalist design and reliance on unlicensed spectrum expose vulnerabilities across the entire protocol stack from physical-layer jamming to MAC-layer spoofing and application-layer firmware attacks. Concurrently, the rise of the Artificial Intelligence of Things (AIoT) introduces opportunities to reinforce LoRaWAN security via decentralized, intelligent, and adaptive mechanisms. This paper presents a systematic mapping study of 81 peer-reviewed publications (2020–2025), conducted using a PRISMA-based methodology. Our objectives are to: (1) identify key trends and research directions in LoRaWAN security, (2) propose a MITRE ATT&CK-inspired taxonomy tailored to the LoRaWAN stack, (3) analyze AIoT-based security contributions, and (4) highlight unresolved challenges and future perspectives. Our findings indicate that 62% of documented cyberattacks target the MAC layer, exploiting vulnerabilities such as static keys and weak integrity checks. AI-driven techniques including RF fingerprinting (97% accuracy using CNNs), federated learning for anomaly detection, and blockchain-based key management—show promise but raise concerns about scalability and deployment on constrained devices. We introduce the first MITRE ATT&CK-LoRaWAN matrix, detailing 18 attack techniques (e.g., energy depletion, rogue gateways) and associated countermeasures, including post-quantum Kyber-1024 encryption. Finally, we discuss major technical, methodological, and interoperability challenges, and suggest actionable research directions toward secure, AI-native, and resilient LoRaWAN infrastructures.
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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 | 0.012 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".