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Tala Abdulrahman Abed

dblp:392/9873 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0009-2064-0754ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Blockchain and cryptocurrency security · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Blockchain and cryptocurrency security
decentralized finance
0.912025
Maximal Extractable Value in Decentralized Finance: Taxonomy, Detection, and Mitigation · IEEE Trans. Serv. Comput. 2025
Blockchain and cryptocurrency security
maximal extractable value
0.912025
Maximal Extractable Value in Decentralized Finance: Taxonomy, Detection, and Mitigation · IEEE Trans. Serv. Comput. 2025

Methods — techniques the papers use, named apart from their topics

taxonomy · 0.9comparative analysis · 0.9
YearPublicationVenuePosition
2025 Maximal Extractable Value in Decentralized Finance: Taxonomy, Detection, and Mitigation
abstract
Decentralized Finance (DeFi) leverages blockchain-enabled smart contracts to deliver automated and trustless financial services without the need for intermediaries. However, the public visibility of financial transactions on the blockchain can be exploited, as participants can reorder, insert, or remove transactions to extract value, often at the expense of others. This extracted value is known as the Maximal Extractable Value (MEV). MEV causes financial losses and consensus instability, disrupting the security, efficiency, and decentralization goals of the DeFi ecosystem. Therefore, it is crucial to analyze, detect, and mitigate MEV to safeguard DeFi. Our comprehensive survey offers a holistic view of the MEV landscape in the DeFi ecosystem. We present an in-depth understanding of MEV through a novel taxonomy of MEV transactions supported by real transaction examples. We perform a critical comparative analysis of various MEV detection approaches, evaluating their effectiveness in identifying different transaction types. Furthermore, we assess different categories of MEV mitigation strategies and discuss their limitations. We identify the challenges of current mitigation and detection approaches and discuss potential solutions. This survey provides valuable insights for researchers, developers, stakeholders, and policymakers, helping to curb and democratize MEV for a more secure and efficient DeFi ecosystem.
Huned Materwala, Shraddha M. Naik, Aya Taha, Tala Abdulrahman Abed, Davor Svetinovic
IEEE Trans. Serv. Comput.4