A Large-Scale Analysis of IoT Firmware Version Distribution in the Wild
This paper examines the up-to-dateness of installed firmware versions of IoT devices accessible via public internet. It analyzes datasets of 1.06m devices collected from the IoT search engine Censys and maps the results against the latest firmware version each manufacturer offers. By applying the SEMMA data mining process, a fully scalable and adaptive approach is developed. This approach relies on three data artifacts: raw data from Censys, a mapping table with firmware versions and a keyword search list. The preliminary results confirm the heterogeneity of connected IoT devices. They show that manufacturer, device type and country influence the up-to-dateness of firmware. The results suggest users as a "weak link" as they do not update the firmware of their devices in a timely manner. However, the heterogeneity leads to results not showing a high reliability, yet.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Efficient blockchain-based IoT firmware update considering distribution incentives
The number of Internet of Things (IoT) devices has increased recently and will increase further in the future. IoT devices are vulnerable, so vendors update and distribute firmware. At the time of firmware distributio…
ManagementDetecting Architectural Drift in Safety-Critical Firmware through Runtime Trace Analysis
Maintaining consistency between architectural design and runtime-observed behavior is challenging in long-lived safety-critical firmware. This paper presents a runtime-informed methodology for detecting architectural dri…
RVDebloater: Mode-based Adaptive Firmware Debloating for Robotic Vehicles
As the number of embedded devices grows and their functional requirements increase, embedded firmware is becoming increasingly larger, thereby expanding its attack surface. Despite the increase in firmware size, many emb…
Incremental Firmware Update Over-the-Air for Low-Power IoT Devices over LoRaWAN
Efficiently supporting remote firmware updates in Internet of Things (IoT) devices remains a significant challenge due to the limitations of many IoT communication protocols, which often make it impractical to transmit f…
Securing LLM-Generated Embedded Firmware through AI Agent-Driven Validation and Patching
Large Language Models (LLMs) show promise in generating firmware for embedded systems, but often introduce security flaws and fail to meet real-time performance constraints. This paper proposes a three-phase methodology …