{"id":18285,"date":"2026-06-05T14:41:15","date_gmt":"2026-06-05T12:41:15","guid":{"rendered":"https:\/\/www.uni.lu\/research-en\/?post_type=events&p=18285"},"modified":"2026-06-15T13:40:10","modified_gmt":"2026-06-15T11:40:10","slug":"doctoral-defence-bahareh-parhizkari","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/research-en\/events\/doctoral-defence-bahareh-parhizkari\/","title":{"rendered":"Doctoral Defence: Bahareh PARHIZKARI"},"content":{"rendered":"\n
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The Doctoral School in Science and Engineering<\/a> is happy to invite you to Bahareh PARHIZKARI\u2019s defence entitled<\/p>\n\n\n\n

\nProactive Detection of Malicious Activities in Decentralized Finance Through Analysis of Behavioral Attack Patterns<\/h2>\n\n\n\n

Supervisor<\/strong>: Prof Radu STATE<\/a><\/p>\n\n\n\n

Security incidents have become a major challenge in decentralized finance (DeFi), resulting in substantial financial losses and raising concerns about the resilience of blockchain-based financial ecosystems. While existing security mechanisms have improved the detection of malicious activities, many approaches rely on signals that become observable only shortly before or during attack execution.<\/p>\n\n\n\n

This dissertation is motivated by the observation that malicious activities frequently leave detectable traces before exploit execution. It investigates how security-relevant signals emerge throughout different stages of the DeFi attack lifecycle and examines their potential for proactive threat detection. To this end, the research explores multiple sources of security intelligence, including attacker preparation activities, smart contract deployment processes, behavioral patterns associated with the evolution of DeFi projects, and information derived from both on-chain and off-chain environments.<\/p>\n\n\n\n

The results presented throughout the dissertation indicate that valuable security signals can often be identified before attacks become observable on-chain. Collectively, the findings demonstrate the feasibility of proactive threat detection in DeFi and highlight opportunities for mitigation before exploit execution occurs. By shifting the focus from attack detection during execution to the identification of early-stage indicators of malicious intent, this dissertation contributes toward more resilient security mechanisms for decentralized finance ecosystems.<\/p>\n\n\n\n