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A Brain-Inspired Trust Management Model to Assure Security in a Cloud based IoT Framework for Neuroscience Applications
Mufti Mahmud, M. Shamim Kaiser, M. Mostafizur Rahman, M. Arifur Rahman, Antesar Shabut, Shamim Al-Mamun, Amir Hussain
TL;DR
Variable neuroscience signals and behavior-only trust checks leave reliable IoT communication a challenge. The paper proposes a brain-inspired model combining behavioral and data trust, and NS2 simulations report better performance than several trust-management alternatives.
Problem
Neuroscience signals are highly variable, and behavior-only malicious-transmission detection is insufficient to ensure reliable communication, motivating trust assessment that also accounts for data similarity.
Source: Introduction · p. 3Method
The proposed model evaluates behavioral trust with an Adaptive Neuro-Fuzzy model and data trust with a weighted-additive method for IoT devices and relay nodes.
Source: Introduction · p. 4, §5 · p. 13Results
NS2 simulations report better performance than FIS, NFTM, and other trust-management algorithms across packet forwarding ratio, throughput, AECR, and accuracy.
Source: §5 · p. 13Takeaways & Limitations
The model is proposed to secure data transmission and ensure data reliability in cloud-based IoT architectures for neuroscience applications.
Source: §5 · p. 13
Abstract
from arXiv · showhide
Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructure, trust management is needed at the IoT and user ends. This paper introduces a Neuro-Fuzzy based Brain-inspired trust management model (TMM) to secure IoT devices and relay nodes, and to ensure data reliability. The proposed TMM utilizes node behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference System and weighted-additive methods respectively to assess the nodes trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2 simulation results confirm the robustness and accuracy of the proposed TMM in identifying malicious nodes in the communication network. With the growing usage of cloud based IoT frameworks in Neuroscience research, integrating the proposed TMM into the existing infrastructure will assure secure and reliable data communication among the E2E devices.
Introduction
Neuroscience research increasingly relies on cloud-connected IoT, while variable brain signals and the limits of behavior-only trust assessment make reliable, secure communication a challenge. The paper proposes a trust model that evaluates both node behavior and data trust.
Introduction
- Cloud computing and IoT support data-intensive neuroscience research by connecting sensors to cloud resources for analysis and resource sharing.The introduction describes cloud computing as facilitating computationally intensive analytics and modeling.Source: Introduction · p. 2
- Neuroscience signals are highly variable, making unsupervised acquisition more prone to misidentification, misclassification, and misinterpretation and requiring extra care with IoT-node trustworthiness.The paper contrasts neuroscience signals with biosignals where periodicities and similarities have been observed.Source: Introduction · p. 3
- Behavior-based malicious-transmission detection alone is insufficient; reliable communication also requires accounting for whether node-generated data are error-free.The paper identifies data similarity alongside node behavior as a possible basis for trust management.Source: Introduction · p. 3
- Prior work includes behavior-based trust models, but the paper identifies the need to assess both node behavior and data similarity for reliable communication.The related-work discussion describes several trust-management approaches based on behavior, data, energy, or other trust properties.Source: Introduction · p. 3
- The paper proposes a brain-inspired Adaptive Neuro-Fuzzy trust model for cloud-based neuroscience IoT that evaluates behavioral and data trust for devices and relay nodes.The work also investigates trust management’s effects on quality-of-service issues in the architecture.Source: Introduction · p. 4
1 Cloud based IoT Architecture
The architecture links neuroscience IoT devices, cloud services, and users, while assuming cloud communications are secured through existing authentication. Trust evaluation uses a sequence of trust composition, propagation, aggregation, and update.
1 Cloud based IoT Architecture
- The neuroscience IoT architecture has IoT, cloud, and user ends, with the cloud providing connectivity, processing, and analysis and the user end accessing processed data.IoT devices send data through an IoT gateway for analytics or storage.Source: §1 · pp. 4–5
- The architecture assumes cloud communications are secured through existing certification and authentication models, leaving IoT and user ends as the trust-management focus.The stated aim is to ensure end-to-end trust among IoT devices and users.Source: §1 · p. 5
- Trust evaluation proceeds through trust compositions, propagation, aggregation, and update; Figure 4 depicts the steps.Trust metrics may be aggregated using a static weighted sum, neuro-fuzzy method, or Bayesian inference, and updates may be periodic for energy efficiency.Source: §1 · p. 5, Figure 4 · p. 5
- Figure 5 aggregates data-trust and behavioral-trust values to determine trust levels for sensor and relay nodes.The model evaluates IoT devices and relay nodes to discard malicious nodes.Source: Figure 5 · p. 6
2 Trust Management Model
The model evaluates IoT devices and relay nodes using profile information, behavioral trust, and data trust. Authentication validates profiles, while ANFIS and weighted-additive methods estimate behavioral and data trust, respectively.
2 Trust Management Model
- IoT devices and relay nodes are evaluated using profile information, behavioral trust, and data trust; authentication validates profiles, and ANFIS and weighted-additive methods estimate the latter two.Source: §2 · p. 6
2.1.1 Behavioral Trust Metrics
- RFI reflects interaction frequency, intimacy reflects relationship duration, and honesty is estimated from successful and unsuccessful interactions.Source: §2.1.1 · p. 7
2.1.2 Node Behavioral Trust
- Behavioral trust combines direct assessment from prior interactions with indirect trust from neighboring guarantors, accounting for each guarantor’s hop count.Source: §2.1.2 · pp. 7–8
2.1.3 ANFIS based Node Behavioral Trust Model
- The ANFIS uses RFI, Intimacy, and Honesty, each with Low, Medium, and High membership functions, across 19 possible rules to output behavioral trust; it is trained on NS2 data for 50 nodes.Source: §2.1.3 · pp. 8–9
2.2 Evaluation of Data Trust
- Data trust combines direct and indirect estimates of deviation from historical data; the indirect estimate assumes the included nodes are trusted.Source: §2.2 · pp. 9–10
3 Performance Metrics
The section evaluates the proposed Brain-inspired TMM using five metrics spanning packet delivery, throughput, energy consumption, accuracy, and classification balance.
3 Performance Metrics
- The proposed Brain-inspired TMM is evaluated using PFR, NetT, AECR, accuracy, and F-measure.These metrics cover packet delivery, throughput, energy consumption, accuracy, and the balance of precision and recall.Source: §3 · p. 10
- PFR is the ratio of packets received by the IoT-CPS to packets transmitted by the source, and it decreases when forwarded packets are dropped.The stated causes of drops include buffer overflow, blocking, and route failure.Source: §3 · p. 10
- NetT measures the rate of successful source transmissions delivered to the destination and declines with the appearance of misbehaving nodes.It is calculated from successful transmissions delivered to the destination over the considered transmission interval.Source: §3 · p. 10
- AECR is the ratio of trust-evaluation energy to data-transmission energy, and is lower for malicious than legitimate nodes.The paper attributes the difference to malicious nodes not participating in packet forwarding or route discovery.Source: §3 · p. 10
- Accuracy is defined as the ratio of successful interactions to total interactions.The passage also expresses accuracy using TP, TN, FP, and FN categories.Source: §3 · p. 11
- F-measure is the harmonic mean of precision and recall.The paper identifies precision and recall as measures used to evaluate classification outcomes.Source: §3 · p. 11
4 Results
In NS-2 simulations, the proposed TMM outperformed trusted AODV and AODV without trust in network throughput, and outperformed TRM in packet forwarding and energy consumption. It also exceeded FIS in accuracy and F-measure.
4 Results
- The evaluation averaged 20 NS-2 simulation runs using AODV, with legitimate and malicious nodes modeled in route discovery and packet forwarding.Legitimate nodes participated in both processes, while malicious nodes participated in neither.Source: §4 · p. 11
- The model evaluated node behavior trust and direct and indirect data trust after interactions, with nodes initialized to random trust values.Source: §4 · p. 11
- PFR decreased as malicious nodes increased from 10% to 50%, but TMM outperformed TRM and both trust-managed approaches outperformed AODV without trust.Malicious nodes were defined as nodes that hid during route discovery or intentionally dropped packets.Source: §4 · pp. 11–12, Figure 8 · p. 12
- The proposed TMM outperformed trusted AODV and AODV without trust in overall network throughput.The comparison used AODV-TMM, TAODV, and AODV; throughput fell as malicious nodes increased from 10% to 50%.Source: Figure 9 · p. 12, §4 · p. 12
- TMM consumed less energy than TRM as malicious nodes increased from 10% to 50%, and its reduced AECR indicated greater malicious-node identification.Source: §4 · pp. 12–13, Figure 10 · p. 13
- TMM accuracy was 0.967 with five linguistic terms and 0.957 with three, versus FIS accuracy of 0.89; F-measure was 0.97 and 0.96, versus 0.90 for FIS.Source: Table 2 · p. 13, §4 · p. 13
5 Conclusion and Future Work
The conclusion presents a brain-inspired trust management model for cloud-based IoT neuroscience applications. The authors report stronger performance than several trust-management alternatives and propose more sophisticated methods for future work.
5 Conclusion and Future Work
- The proposed TMM jointly evaluates node behavioral trust and data trust using ANFIS and a weighted-additive method, then constructs a list of trustworthy nodes.Source: §5 · p. 13
- NS-2 simulations showed better performance than FIS, NFTM, and other trust-management algorithms across PFR, throughput, AECR, accuracy, and F-measure.Source: §5 · p. 13
- Future work proposes optimization techniques and Bayesian, deep-learning, and reinforcement-learning approaches for trust management.Source: §5 · p. 13