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Unsupervised Learning for Trustworthy IoT
Open accessAbstract
The advancement of Internet-of-Things (IoT) edge devices with various types of sensors enables us to harness diverse information with Mobile Crowd-Sensing applications (MCS). This highly dynamic setting entails the collection of ubiquitous data traces, originating from sensors carried by people, introducing new information security challenges; one of them being the preservation of data trustworthiness. What is needed in these settings is the timely analysis of these large datasets to produce accurate insights on the correctness of user reports. Existing data mining and other artificial intelligence methods are the most popular to gain hidden insights from IoT data, albeit with many challenges. In this paper, we first model the cyber trustworthiness of MCS reports in the presence of intelligent and colluding adversaries. We then rigorously assess, using real IoT datasets, the effectiveness and accuracy of well-known data mining algorithms when employed towards IoT security and privacy. By taking into account the spatio-temporal changes of the underlying phenomena, we demonstrate how concept drifts can masquerade the existence of attackers and their impact on the accuracy of both the clustering and classification processes. Our initial set of results clearly show that these unsupervised learning algorithms are prone to adversarial infection, thus, magnifying the need for further research in the field by leveraging a mix of advanced machine learning models and mathematical optimization techniques.
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“the malfunction of the Maneuvering Characteristics Augmentation System (MCAS) on Boeing air-crafts led to the crash of two aircrafts which resulted in the fatalities of 157 and 189 passengers, respectively [82], [83].”
From A Review of Trustworthy and Explainable Artificial Intelligence (XAI) (2023) · cites this paper as [83]
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Cited by
Sorted by citations
A Review of Trustworthy and Explainable Artificial Intelligence (XAI)
Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access · doi:10.1109/access.2023.3294569 · Cited by 352
Metadata
- Authors
- Banerjee, N., Giannetsos, T., Panaousis, E., & Took, C. C.
- Affiliations
- University of Surrey (GB)
- Venue
- 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 1-8
- ISSN
- 1098-7584 (ISSN-L)
- OpenAlex
- W2804163247
- Cited by
- 3
- References
- 0
- Citations by year
- 2023: 1 · 2019: 1 · 2018: 1
- FWCI
- 0.20
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Surrey Research Insight Open Access (The University of Surrey) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council (EP/R006938/1, EP/N002717/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Science Applications › Mobile Crowdsensing and Crowdsourcing
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- Internet of Things security, mobile crowdsensing, data trustworthiness, unsupervised learning, colluding adversaries, concept drift, clustering, classification, data mining, privacy, spatio-temporal data
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