Authentication Protocols for IoT Edge Computing
Bibliographic record
Abstract
The proliferation of IoT has led to vast interconnectivity, generating massive data that exceeds the processing capabilities of IoT devices. Traditional IoT-cloud models, where devices offload computations to centralized cloud servers, are increasingly inadequate due to the expected surge in IoT devices, projected to surpass 75 billion by 2025. This growth intensifies cloud vulnerability to single points of failure and highlights the need for alternatives that meet QoS requirements like low latency and location awareness. The 3-tier IoT-edge-cloud architecture offers a solution by processing data at nearby edge nodes, improving location awareness, and mitigating single-point-of-failure. While this distributed approach meets QoS requirements, it introduces security challenges, such as offloading data to distributed edge nodes without prior registration. Additionally, an adversary can trace the edge node attached to the IoT device and compromise the privacy of an IoT device user. Moreover, many deployed IoT devices are vulnerable to hardware compromise and unauthorized access, raising significant privacy and security concerns that hinder the broader adoption of edge computing. In this thesis, we address the above challenges by proposing efficient and secure authentication protocols for IoT applications in edge computing. Our proposed protocols include Symmetric Key Authentication with Forward Secrecy (SKAFS), Symmetric Key Inter-Cloud Authentication and Redeemable Micropayment Protocol (SKICAP), Mutual Authentication Privacy-Preserving Protocol with Forward Secrecy (MAPFS), and Conditional Privacy-Preserving Message Authentication for VANET Emergency Exchange (CP-MAVE). The proposed protocols utilize lightweight cryptographic primitives to realize efficient protocols for edge computing. Moreover, the proposed protocols fulfill the security requirements for IoT applications, such as IoT device anonymity, session unlinkability, and resilience to hardware compromise of IoT devices. For our proposed protocols, we provided formal security analyses based on computationally hard problems. Furthermore, we evaluated their performance in terms of communication overhead and computational complexity and compared them with other closely related protocols. Finally, we implemented prototypes of our proposed protocols using socket programming, simulating the message flow between the protocol entities to calculate their end-to-end latency and confirm the efficiency of our proposed protocols. The proliferation of IoT has led to vast interconnectivity, generating massive data that exceeds the processing capabilities of IoT devices. Traditional IoT-cloud models, where devices offload computations to centralized cloud servers, are increasingly inadequate due to the expected surge in IoT devices, projected to surpass 75 billion by 2025. This growth intensifies cloud vulnerability to single points of failure and highlights the need for alternatives that meet Quality of Service (QoS) requirements like low latency and location awareness. The 3-tier IoT-edge-cloud architecture offers a solution by processing data at nearby edge nodes, improving location awareness, and mitigating single-point-of-failure. While this distributed approach meets QoS requirements, it introduces security challenges, such as offloading data to distributed edge nodes without prior registration. Additionally, an adversary can trace the edge node attached to the IoT device and compromise the privacy of an IoT device user. Moreover, with 2.38 billion IoT devices vulnerable to hardware compromise and unauthorized access, raising significant privacy and security concerns that hinder the broader adoption of edge computing. In this thesis, we address the above challenges by proposing efficient and secure authentication protocols for IoT applications in edge computing. Our proposed protocols include Symmetric Key Authentication with Forward Secrecy (SKAFS), Symmetric Key Inter-Cloud Authentication and Redeemable Micropayment Protocol (SKICAP), Mutual Authentication Privacy-Preserving Protocol with Forward Secrecy (MAPFS), and Conditional Privacy-Preserving Message Authentication for VANET Emergency Exchange (CP-MAVE). The proposed protocols utilize lightweight cryptographic primitives to realize efficient protocols for edge computing. Moreover, the proposed protocols fulfill the security requirements for IoT applications, such as IoT device anonymity, session unlinkability, and resilience to hardware compromise of IoT devices. For our proposed protocols, we provided formal security analyses based on computationally hard problems. Furthermore, we evaluated their performance in terms of communication overhead and computational complexity and compared them with other closely related protocols. Finally, we implemented prototypes of our proposed protocols using socket programming, simulating the message flow between the protocol entities to calculate their end-to-end latency and confirm the efficiency of our proposed protocols.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".