2 Search Results for "Yan, Rundong"

Document
DOI: 10.4230/LIPIcs.AFT.2025.35
On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation

Authors: Abdulrahman Alhaidari, Balaji Palanisamy, and Prashant Krishnamurthy

Published in: LIPIcs, Volume 354, 7th Conference on Advances in Financial Technologies (AFT 2025)

Abstract
Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses focus on static code analysis, public mempool screening, attacker contract detection, or trusted off-chain monitors, none of which prevents exploits submitted through private relays or malicious contracts that execute within the same block. We present the first decentralized, fully on-chain learning framework that: (i) performs gas-prohibitive computation on Layer-2 to reduce cost, (ii) propagates verified model updates to Layer-1, and (iii) enables gas-bounded, low-latency inference inside smart contracts. A novel Proof-of-Improvement (PoIm) protocol governs the training process and verifies each decentralized micro update as a self-verifying training transaction. Updates are accepted by PoIm only if they demonstrably improve at least one core metric (e.g., accuracy, F1-score, precision, or recall) on a public benchmark without degrading any of the other core metrics, while adversarial proposals get financially penalized through an adaptable test set for evolving threats. We develop quantization and loop-unrolling techniques that enable inference for logistic regression, SVM, MLPs, CNNs, and gated RNNs (with support for formally verified decision tree inference) within the Ethereum block gas limit, while remaining bit-exact to their off-chain counterparts, formally proven in Z3. We curate 298 unique real-world exploits (2020 - 2025) with 402 exploit transactions across eight EVM chains, collectively responsible for $3.74 B in losses. We demonstrate that on-chain ML governed by PoIm detects previously unseen attacks with over 97% attack detection accuracy and 82.0% F1. A single inference, such as one made via an external call, typically incurs zero cost. Fully on-chain inference consumes 57,603 gas (≈ $0.18) for linear models, 143,647 gas (≈ $0.49) for CNN(F2, K1), and 506,397 gas (≈ $1.77) for CNN(F8, K4) on L1 (e.g., Ethereum). Our results show that practical and continually evolving DeFi defenses can be embedded directly in protocol logic without trusted guardians, and our solution achieves highly cost-effective protection while filling a critical gap between vulnerability scanners and real-time transaction screening.
Cite as

Abdulrahman Alhaidari, Balaji Palanisamy, and Prashant Krishnamurthy. On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation. In 7th Conference on Advances in Financial Technologies (AFT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 354, pp. 35:1-35:27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

Copy BibTex To Clipboard

@InProceedings{alhaidari_et_al:LIPIcs.AFT.2025.35,
  author =	{Alhaidari, Abdulrahman and Palanisamy, Balaji and Krishnamurthy, Prashant},
  title =	{{On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation}},
  booktitle =	{7th Conference on Advances in Financial Technologies (AFT 2025)},
  pages =	{35:1--35:27},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-400-0},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{354},
  editor =	{Avarikioti, Zeta and Christin, Nicolas},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.AFT.2025.35},
  URN =		{urn:nbn:de:0030-drops-247548},
  doi =		{10.4230/LIPIcs.AFT.2025.35},
  annote =	{Keywords: DeFi attacks, on-chain machine learning, decentralized learning, real-time defense}
}
Document
DOI: 10.4230/OASIcs.SCOR.2016.2
Reliability Modelling of Automated Guided Vehicles by the Use of Failure Modes Effects and Criticality Analysis, and Fault Tree Analysis

Authors: Rundong Yan, Sarah J. Dunnett, and Lisa M. Jackson

Published in: OASIcs, Volume 50, 5th Student Conference on Operational Research (SCOR 2016)

Abstract
Automated Guided Vehicles (AGVs) are being increasingly used for intelligent transportation and distribution of materials in warehouses and auto-production lines. In this paper, a preliminary hazard analysis of an AGV's critical components is conducted by the approach of Failure Modes Effects and Criticality Analysis (FMECA). To implement this research, a particular AGV transport system is modelled as a phased mission. Then, Fault Tree Analysis (FTA) is adopted to model the causes of phase failure, enabling the probability of success in each phase and hence mission success to be determined. Through this research, a promising technical approach is established, which allows the identification of the critical AGV components and crucial mission phases of AGVs at the design stage.
Cite as

Rundong Yan, Sarah J. Dunnett, and Lisa M. Jackson. Reliability Modelling of Automated Guided Vehicles by the Use of Failure Modes Effects and Criticality Analysis, and Fault Tree Analysis. In 5th Student Conference on Operational Research (SCOR 2016). Open Access Series in Informatics (OASIcs), Volume 50, pp. 2:1-2:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

Copy BibTex To Clipboard

@InProceedings{yan_et_al:OASIcs.SCOR.2016.2,
  author =	{Yan, Rundong and Dunnett, Sarah J. and Jackson, Lisa M.},
  title =	{{Reliability Modelling of Automated Guided Vehicles by the Use of Failure Modes Effects and Criticality Analysis, and Fault Tree Analysis}},
  booktitle =	{5th Student Conference on Operational Research (SCOR 2016)},
  pages =	{2:1--2:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-004-0},
  ISSN =	{2190-6807},
  year =	{2016},
  volume =	{50},
  editor =	{Hardy, Bradley and Qazi, Abroon and Ravizza, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SCOR.2016.2},
  URN =		{urn:nbn:de:0030-drops-65144},
  doi =		{10.4230/OASIcs.SCOR.2016.2},
  annote =	{Keywords: Reliability, Fault Tree Analysis, Automated Guided Vehicles, FMECA}
}
  • Refine by Type
  • 2 Document/PDF
  • 1 Document/HTML

  • Refine by Publication Year
  • 1 2025
  • 1 2016

  • Refine by Author
  • 1 Alhaidari, Abdulrahman
  • 1 Dunnett, Sarah J.
  • 1 Jackson, Lisa M.
  • 1 Krishnamurthy, Prashant
  • 1 Palanisamy, Balaji
  • Show More...

  • Refine by Series/Journal
  • 1 LIPIcs
  • 1 OASIcs

  • Refine by Classification
  • 1 Security and privacy → Distributed systems security
  • 1 Security and privacy → Network security
  • 1 Security and privacy → Security protocols

  • Refine by Keyword
  • 1 Automated Guided Vehicles
  • 1 DeFi attacks
  • 1 FMECA
  • 1 Fault Tree Analysis
  • 1 Reliability
  • Show More...

Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2026 Schloss Dagstuhl – LZI GmbH Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact