Living in the Dark: MQTT-Based Exploitation of IoT Security Vulnerabilities in ZigBee Networks for Smart Lighting Control
Bibliographic record
Abstract
The Internet of Things (IoT) has provided substantial enhancements to the communication of sensors, actuators, and their controllers, particularly in the field of home automation. Home automation is experiencing a huge rise in the proliferation of IoT devices such as smart bulbs, smart switches, and control gateways. However, the main challenge for such control systems is how to maximize security under limited resources such as low-processing power, low memory, low data rate, and low-bandwidth IoT networks. In order to address this challenge the adoption of IoT devices in automation has mandated the adoption of secure communication protocols to ensure that compromised key security objectives, such as confidentiality, integrity, and availability are addressed. In light of this, this work evaluates the feasibility of MQTT-based Denial of Service (DoS) attacks, Man-in-the-Middle (MitM), and masquerade attacks on a ZigBee network, an IoT standard used in wireless mesh networks. Performed through MQTT, the attacks extend to compromise neighboring Constrained Application Protocol (CoAP) nodes, a specialized service layer protocol for resource-constrained Internet devices. By demonstrating the attacks on an IKEA TRÅDFRI lighting system, the impact of exploiting ZigBee keys, the basis of ZigBee security, is shown. The reduction of vulnerabilities to prevent attacks is imperative for application developers in this domain. Two Intrusion Detection Systems (IDSs) are proposed to mitigate against the proposed attacks, followed by recommendations for solution providers to improve IoT firmware security. The main motivation and purpose of this work is to demonstrate that conventional attacks are feasible and practical in commercial home automation IoT devices, regardless of the manufacturer. Thus, the contribution to the state-of-the-art is the design of attacks that demonstrate how known vulnerabilities can be exploited in commercial IoT devices for the purpose of motivating manufacturers to produce IoT systems with improved security.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".