Blockchain and explainable AI for enhanced decision making in cyber threat detection
Open accessAbstract
Summary: Artificial Intelligence (AI) based cyber threat detection tools are widely used to process and analyze a large amount of data for improved intrusion detection performance. However, these models are often considered as black box by the cybersecurity experts due to their inability to comprehend or interpret the reasoning behind the decisions. Moreover, AI‐based threat hunting is data‐driven and is usually modeled using the data provided by multiple cloud vendors. This is another critical challenge, as a malicious cloud can provide false information (i.e., insider attacks) and can degrade the threat‐hunting capability. In this paper, we present a blockchain‐enabled eXplainable AI (XAI) for enhancing the decision‐making capability of cyber threat detection in the context of Smart Healthcare Systems. Specifically, first, we use blockchain to validate and store data between multiple cloud vendors by implementing a Clique Proof‐of‐Authority (C‐PoA) consensus. Second, a novel deep learning‐based threat‐hunting model is built by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multi‐head attention mechanism for improved attack detection. The extensive experiment confirms its potential to be used as an enhanced decision support system by cybersecurity analysts.
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These scholars frequently contribute to hybrid model architectures blending statistical learning with deep generative or sequence-based techniques.
“These scholars frequently contribute to hybrid model architectures that blend statistical learning with deep generative or sequencebased techniques, as reflected in [35,50].”
From Application of Machine Learning and Deep Learning Techniques for Enhanced Insider Threat Detection in Cybersecurity: Bibliometric Review (2025) · cites this paper as [50]
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- Authors
- Kumar, P., Javeed, D., Kumar, R., & Islam, A. K. M. N.
- Affiliations
- Lappeenranta-Lahti University of Technology (FI)
- Northeastern University (CN)
- SRM University (IN)
- Corresponding author
- Kumar, Prabhat
- Venue
- Software: Practice and Experience
- Publisher
- Wiley
- Published
- 2024-02-19
- Volume
- 54
- Issue
- 8
- Pages
- 1337-1360
- ISSN
- 0038-0644 (ISSN-L)
- 1097-024X
- OpenAlex
- W4392782431
- Cited by
- 58
- References
- 38
- Citations by year
- 2026: 17 · 2025: 34 · 2024: 7
- FWCI
- 9.66
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Software Practice and Experience (publishedVersion, cc-by)
- Greater South Information System
- Funders
- Academy of Finland (359790)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- blockchain, explainable AI, cyber threat detection, threat hunting, intrusion detection, decision support, smart healthcare, insider attacks, data validation, stacked LSTM, multi-head attention, attack detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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