Adaptive Parameterisation for Efficient Detection of False Data Injections Article Swipe
Akash Bhattacharya
,
Sunandan Adhikary
,
Ipsita Koley
,
Vivek Loya
,
Soumyajit Dey
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3777903
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1145/3777903
Increasing interconnectivity in modern safety-critical cyber-physical systems (CPSs) renders them susceptible to attacks like false data injection (FDI). Due to computation and communication resource constraints, it is infeasible to encrypt all data exchanges in such systems. As the other alternative, the lightweight statistical detectors are system-agnostic in nature; attackers can launch stealthy FDI attacks with a high degree of sophistication. This research introduces an adaptive parameterisation method for stateful anomaly detectors to fill this security gap. The study includes a theoretical analysis for statistical evidence of stealthy data falsifications. The proposed adaptive detection framework has the capability to continuously observe the system’s behaviour in real time, with the goal of rapidly detecting FDI incidents using this statistical evidence. We propose a novel parameter tuning strategy to guarantee early detection of FDI attacks, keeping the false alarms to a minimum. Its efficacy is evaluated in CPS case studies from different domains and in an automotive Hardware-in-the-Loop (HIL) setup.
Related Topics
Concepts
Computer science
Anomaly detection
Data mining
Interconnectivity
Intrusion detection system
Detector
Computation
Statistical model
Encryption
Real-time computing
Resource (disambiguation)
Stateful firewall
Statistical hypothesis testing
Statistical analysis
Reliability (semiconductor)
Data modeling
Error detection and correction
Anomaly (physics)
Data security
Algorithm
Automotive industry
Synthetic data
Root cause
Rare events
Resource constraints
Artificial intelligence
Metadata
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- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3777903
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- References
- 32
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https://doi.org/10.1145/3777903Digital Object Identifier
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Adaptive Parameterisation for Efficient Detection of False Data InjectionsWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-11-21Full publication date if available
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Akash Bhattacharya, Sunandan Adhikary, Ipsita Koley, Vivek Loya, Soumyajit DeyList of authors in order
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https://doi.org/10.1145/3777903Publisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1145/3777903Direct OA link when available
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Computer science, Anomaly detection, Data mining, Interconnectivity, Intrusion detection system, Detector, Computation, Statistical model, Encryption, Real-time computing, Resource (disambiguation), Stateful firewall, Statistical hypothesis testing, Statistical analysis, Reliability (semiconductor), Data modeling, Error detection and correction, Anomaly (physics), Data security, Algorithm, Automotive industry, Synthetic data, Root cause, Rare events, Resource constraints, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| primary_location.license_id | https://openalex.org/licenses/cc-by |
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| primary_location.raw_source_name | ACM Transactions on Embedded Computing Systems |
| primary_location.landing_page_url | https://doi.org/10.1145/3777903 |
| publication_date | 2025-11-21 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W3097420500, https://openalex.org/W3178991481, https://openalex.org/W2056451850, https://openalex.org/W2950787783, https://openalex.org/W2536935267, https://openalex.org/W3115892855, https://openalex.org/W2803355698, https://openalex.org/W2402911760, https://openalex.org/W4391786690, https://openalex.org/W3134772926, https://openalex.org/W4200255682, https://openalex.org/W4400189350, https://openalex.org/W3036399443, https://openalex.org/W2039427951, https://openalex.org/W3041482827, https://openalex.org/W2024673847, https://openalex.org/W1965180610, https://openalex.org/W2807864928, https://openalex.org/W2567728531, https://openalex.org/W2911595693, https://openalex.org/W3000053734, https://openalex.org/W2051903196, https://openalex.org/W2963516826, https://openalex.org/W4210791054, https://openalex.org/W2342670290, https://openalex.org/W4291127740, https://openalex.org/W2781896072, https://openalex.org/W2784151957, https://openalex.org/W1557652964, https://openalex.org/W2153775930, https://openalex.org/W1972523481, https://openalex.org/W2003677466 |
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