Attacks and Defenses in Short-Range Wireless Technologies for IoT
Bibliographic record
Abstract
The Internet of Things, abbreviated as IoT, is a new networking paradigm composed of wireless and wired networks, geographically distributed and interconnected by a “secured” backbone, essentially, the Internet. It connects billions of heterogeneous devices, called Things, using different communication technologies and provides end-users, all over the world, with a variety of smart applications. IoT constitutes a new evolution for the Internet in terms of diversity, size, and applications. It also invites cybercriminals who exploit IoT infrastructures to conduct large scale, distributed, and devastating cyberattacks that may have serious consequences. The security of IoT infrastructures strongly depends on the security of its wired and wireless infrastructures. Still, the wireless infrastructures are thought to be the most outspread, important, and vulnerable part of IoT. To achieve the security goals in the wireless infrastructures of IoT, it is crucial to have a comprehensive understanding of IoT attacks, their classification, and security solutions in such infrastructures. In this paper, we provide a survey of attacks related to the wireless infrastructures of IoT in general, and to the most used short-range wireless communication technologies in the resource-constrained part of IoT in particular. Namely, we consider Wi-Fi, Bluetooth, ZigBee, and RFID wireless communication technologies. The paper also provides a taxonomy of these attacks based on a security service-based attack classification and discusses existing security defenses and mechanisms that mitigate certain attacks as well as the limitations of these security mechanisms.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".